Aussie AI

Prompt Engineering: Types and Optimizations

Last Updated 29 August, 2025

by David Spuler, Ph.D.

Optimizing Prompt Engineering

There are various simple ways to get better results from LLMs with prompt engineering techniques with a single prompt:

Be specific
Give examples
Write longer prompts

Some more advanced approaches include:

Give multiple examples (few-shot prompting)
Negative prompting (tell the AI what not to do)
Personas
Chain-of-thought ("step-by-step" requests)
Specify an output format
Specify a tone, reading level, or other text meta-attribute.

There are various ways to follow up with additional prompts:

Iterative prompting (improve the next prompt based on the previous answer)
Ask the LLM to explain its reasoning
Ask the LLM to evaluate its own answer ("reflection")

Types of Prompt Engineering

The general categories of prompt engineering techniques are:

Zero-shot prompting — no examples.
One-shot prompting — one example.
Few-shot prompting — multiple examples in the prompt.

There are various known effective ways to improve the results in terms of answer accuracy/perplexity using prompt engineering:

Emotional prompting
"Step-by-step" prompting (zero-shot CoT)
Skeleton-of-thought
Chain-of-Thought (CoT) (few-shot)
Tree-of-Thought (ToT)

Surveys on Prompting Techniques

Survey papers on prompt engineering:

Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, Pranav Sandeep Dulepet, Saurav Vidyadhara, Dayeon Ki, Sweta Agrawal, Chau Pham, Gerson Kroiz, Feileen Li, Hudson Tao, Ashay Srivastava, Hevander Da Costa, Saloni Gupta, Megan L. Rogers, Inna Goncearenco, Giuseppe Sarli, Igor Galynker, Denis Peskoff, Marine Carpuat, Jules White, Shyamal Anadkat, Alexander Hoyle, Philip Resnik, 6 Jun 2024, The Prompt Report: A Systematic Survey of Prompting Techniques, https://arxiv.org/abs/2406.06608
Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang, 21 Nov 2023, Prompting Frameworks for Large Language Models: A Survey, https://arxiv.org/abs/2311.12785
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha, 5 Feb 2024, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, https://arxiv.org/abs/2402.07927
Yuan-Feng Song, Yuan-Qin He, Xue-Fang Zhao, Han-Lin Gu, Di Jiang, Hai-Jun Yang, Li-Xin Fan, July 2024, A communication theory perspective on prompting engineering methods for large lan guage models. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(4): 984−1004 July 2024. DOI: 10.1007/s11390-024-4058-8, https://doi.org/10.1007/s11390-024-4058-8 https://jcst.ict.ac.cn/en/article/pdf/preview/10.1007/s11390-024-4058-8.pdf
Vishal Rajput, Oct 2024, The Prompt Report: Prompt Engineering Techniques, https://medium.com/aiguys/the-prompt-report-prompt-engineering-techniques-254464b0b32b

Emotional Prompting

Researchers discovered a weird technique: adding emotion to prompts makes LLMs do better. It's unclear how or why this works, but perhaps it triggers more attention paid to more important sources (i.e., tokens and weights), or perhaps it reduces attention to casual type documents.

Research papers on emotional prompting:

Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie, 12 Nov 2023 (v7), Large Language Models Understand and Can be Enhanced by Emotional Stimuli, https://arxiv.org/abs/2307.11760 https://llm-enhance.github.io/
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha, 5 Feb 2024, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, https://arxiv.org/abs/2402.07927
Chenggian Ma, Xiangyu Zhao, Chunhui Zhang, Yanzhao Qin, Wentao Zhang, 16 Apr 2024, When Emotional Stimuli meet Prompt Designing: An Auto-Prompt Graphical Paradigm, https://arxiv.org/abs/2404.10500
Yarik Menchaca Resendiz, Roman Klinger, 9 Aug 2023, Emotion-Conditioned Text Generation through Automatic Prompt Optimization, https://arxiv.org/abs/2308.04857
Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie, 7 Jun 2024 (v3), The Good, The Bad, and Why: Unveiling Emotions in Generative AI, https://arxiv.org/abs/2312.11111
Mike Taylor Oct 29, 2024, Five proven prompt engineering techniques (and a few more-advanced tactics), https://www.lennysnewsletter.com/p/five-proven-prompt-engineering-techniques
Ziqi Yin, Hao Wang, Kaito Horio, Daisuke Kawahara, Satoshi Sekine, 14 Oct 2024 (v2), Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance, https://arxiv.org/abs/2402.14531
Guanrou Yang, Chen Yang, Qian Chen, Ziyang Ma, Wenxi Chen, Wen Wang, Tianrui Wang, Yifan Yang, Zhikang Niu, Wenrui Liu, Fan Yu, Zhihao Du, Zhifu Gao, ShiLiang Zhang, Xie Chen, 13 Aug 2025, EmoVoice: LLM-based Emotional Text-To-Speech Model with Freestyle Text Prompting, https://arxiv.org/abs/2504.12867

Chain-of-Thought (CoT)

Chain-of-thought prompting is a "step-by-step" prompting method. As a zero-shot technique, it involves just adding an encouragement like "Let's try this step-by-step." to the prompt given to the LLM. As a few-shot prompting technique, it can involve running the LLM through multiple steps to finalize an answer.

Research papers on chain-of-thought:

Jacob Pfau, William Merrill, Samuel R. Bowman, 24 Apr 2024, Let's Think Dot by Dot: Hidden Computation in Transformer Language Models, https://arxiv.org/abs/2404.15758
Hongxuan Zhang, Zhining Liu, Jiaqi Zheng, Chenyi Zhuang, Jinjie Gu, Guihai Chen, Nov 2023, Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster, https://arxiv.org/abs/2311.08263
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe, May 2023, Let's Verify Step by Step, https://arxiv.org/abs/2305.20050
Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin, 13 Jun 2024, Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs, https://arxiv.org/abs/2406.09136 Code: https://github.com/sail-sg/CPO
kipply's blog, 2023-03-30, Transformer Taxonomy (the last lit review), https://kipp.ly/transformer-taxonomy/ (Papers for all the Transformer architectures and milestone papers for the major optimization improvements on them.)
Daniel Lopes, June 21, 2024, A Comprehensive Guide to Text Prompt Engineering Techniques, https://journal.daniellopes.dev/p/practical-prompt-engineering-notes
Wenxiao Wang, Wei Chen, Yicong Luo, Yongliu Long, Zhengkai Lin, Liye Zhang, Binbin Lin, Deng Cai, Xiaofei He, 15 Feb 2024, Model Compression and Efficient Inference for Large Language Models: A Survey, https://arxiv.org/abs/2402.09748
Hao Zhou, Chengming Hu, Ye Yuan, Yufei Cui, Yili Jin, Can Chen, Haolun Wu, Dun Yuan, Li Jiang, Di Wu, Xue Liu, Charlie Zhang, Xianbin Wang, Jiangchuan Liu, 17 May 2024, Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities, https://arxiv.org/abs/2405.10825
Yu Wang, Shiwan Zhao, Zhihu Wang, Heyuan Huang, Ming Fan, Yubo Zhang, Zhixing Wang, Haijun Wang, Ting Liu, 5 Sep 2024, Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation, https://arxiv.org/abs/2409.03271
Asankhaya Sharma (codelion), Sep 2024, Optillm: Optimizing inference proxy for LLMs, https://github.com/codelion/optillm
Ziqi Jin, Wei Lu, 6 Sep 2024, Self-Harmonized Chain of Thought, https://arxiv.org/abs/2409.04057
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha, 5 Feb 2024, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, https://arxiv.org/abs/2402.07927
Shizhe Diao, Pengcheng Wang, Yong Lin, Rui Pan, Xiang Liu, Tong Zhang, 21 Jul 2024 (v5), Active Prompting with Chain-of-Thought for Large Language Models, https://arxiv.org/abs/2302.12246 https://github.com/shizhediao/active-prompt
Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola, 7 Oct 2022, Automatic Chain of Thought Prompting in Large Language Models, https://arxiv.org/abs/2210.03493 https://github.com/amazon-research/auto-cot
Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Guangyuan Piao, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Aidan O'Mahony, Onur Mutlu, Torsten Hoefler, 5 Apr 2024, Demystifying Chains, Trees, and Graphs of Thoughts, https://arxiv.org/abs/2401.14295 http://htor.ethz.ch/publications/img/besta-topologies.pdf
Louis Bouchard, Sep 12, 2024, OpenAI's o1 Model: The Future of Reasoning AI? What Sets It Apart, How OpenAI's o1 Model Thinks Through Problems (And Why It's Slower), https://www.louisbouchard.ai/openai-o1/
OpenAI, September 12, 2024, Learning to Reason with LLMs, https://openai.com/index/learning-to-reason-with-llms/
Emilia David, September 12, 2024, How to prompt on OpenAI’s new o1 models, https://venturebeat.com/ai/how-to-prompt-on-openai-o1/ (Prompt engineering is different for o1, such as "don't use chain of thought.")
Du Phan, Matthew D. Hoffman, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous, 28 Nov 2023, Training Chain-of-Thought via Latent-Variable Inference, https://arxiv.org/abs/2312.02179
Trung Quoc Luong, Xinbo Zhang, Zhanming Jie, Peng Sun, Xiaoran Jin, Hang Li, 27 Jun 2024 (v2), ReFT: Reasoning with Reinforced Fine-Tuning, https://arxiv.org/abs/2401.08967
Tianqiao Liu, Zui Chen, Zitao Liu, Mi Tian, Weiqi Luo, 13 Sep 2024, Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding, https://arxiv.org/abs/2409.08561
Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, Greg Durrett, 18 Sep 2024, To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning, https://arxiv.org/abs/2409.12183
Santosh Kumar Radha, Yasamin Nouri Jelyani, Ara Ghukasyan, Oktay Goktas, 19 Sep 2024, Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning, https://arxiv.org/abs/2409.12618
Artem Shelamanov, Sep 2024, Why OpenAI’s o1 Model Is A Scam, https://pub.towardsai.net/why-openais-o1-model-is-a-scam-eb3356c3d70e
Chung-Yu Wang, Alireza DaghighFarsoodeh, Hung Viet Pham, 24 Sep 2024, Task-oriented Prompt Enhancement via Script Generation, https://arxiv.org/abs/2409.16418
Cassandra A. Cohen, William W. Cohen, 17 Sep 2024, Watch Your Steps: Observable and Modular Chains of Thought, https://arxiv.org/abs/2409.15359
Tongxuan Liu, Wenjiang Xu, Weizhe Huang, Xingyu Wang, Jiaxing Wang, Hailong Yang, Jing Li, 26 Sep 2024, Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models, https://arxiv.org/abs/2409.17539
Zhenwen Liang, Ye Liu, Tong Niu, Xiangliang Zhang, Yingbo Zhou, Semih Yavuz, 5 Oct 2024, Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification, https://arxiv.org/abs/2410.05318
Qiguang Chen, Libo Qin, Jiaqi Wang, Jinxuan Zhou, Wanxiang Che, 8 Oct 2024, Unlocking the Boundaries of Thought: A Reasoning Granularity Framework to Quantify and Optimize Chain-of-Thought, https://arxiv.org/abs/2410.05695 https://github.com/LightChen233/reasoning-granularity
Yingqian Cui, Pengfei He, Xianfeng Tang, Qi He, Chen Luo, Jiliang Tang, Yue Xing, 21 Oct 2024, A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration, https://arxiv.org/abs/2410.16540
Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu, 5 Sep 2024 (v5), Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review, https://arxiv.org/abs/2310.14735
Data Camp, Jul 10, 2024, Chain-of-Thought Prompting: Step-by-Step Reasoning with LLMs, https://www.datacamp.com/tutorial/chain-of-thought-prompting
Pankaj, Dec 21, 2023, Chain of Thought Prompting: Guiding LLMs Step-by-Step, https://medium.com/@pankaj_pandey/chain-of-thought-prompting-guiding-llms-step-by-step-e6eac32d02d8
Jason Wei and Denny Zhou, May 11, 2022, Language Models Perform Reasoning via Chain of Thought, https://research.google/blog/language-models-perform-reasoning-via-chain-of-thought/
Cameron R. Wolfe, Jul 24, 2023, Chain of Thought Prompting for LLMs: A practical and simple approach for “reasoning” with LLMs, https://towardsdatascience.com/chain-of-thought-prompting-for-llms-33c963eead38
Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng, Minghao Liu, Jialong Wu, Jiachen Ma, Yizhi Li, Jian Yang, Wangchunshu Zhou, Qunshu Lin, Junbo Zhao, Zhaoxiang Zhang, Wenhao Huang, Ge Zhang, Chenghua Lin, J.H. Liu, 22 Oct 2024 (v2), A Comparative Study on Reasoning Patterns of OpenAI's o1 Model, https://arxiv.org/abs/2410.13639
Tanay Jaipuria, Oct 29, 2024, OpenAI's o-1 and inference-time scaling laws, https://www.tanayj.com/p/openais-o-1-and-inference-time-scaling
Junda Wu, Xintong Li, Ruoyu Wang, Yu Xia, Yuxin Xiong, Jianing Wang, Tong Yu, Xiang Chen, Branislav Kveton, Lina Yao, Jingbo Shang, Julian McAuley, 31 Oct 2024, OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models, https://arxiv.org/abs/2410.23703
Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, Lili Qiu, 23 Sep 2024, Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely, https://arxiv.org/abs/2409.14924
Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, Li Yuan, 15 Nov 2024, LLaVA-o1: Let Vision Language Models Reason Step-by-Step, https://arxiv.org/abs/2411.10440
Carl Franzen, November 20, 2024, DeepSeek’s first reasoning model R1-Lite-Preview turns heads, beating OpenAI o1 performance, https://venturebeat.com/ai/deepseeks-first-reasoning-model-r1-lite-preview-turns-heads-beating-openai-o1-performance/
Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang, 21 Nov 2024, Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions, https://arxiv.org/abs/2411.14405
Jun Gao, Yongqi Li, Ziqiang Cao, Wenjie Li, 29 Nov 2024, Interleaved-Modal Chain-of-Thought, https://arxiv.org/abs/2411.19488 (Using CoT on a multimodal/vision model.)
Hieu Tran, Zonghai Yao, Junda Wang, Yifan Zhang, Zhichao Yang, Hong Yu, 5 Dec 2024 (v2), RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models, https://arxiv.org/abs/2412.02830
Tiernan Ray, Dec. 10, 2024, How Cerebras boosted Meta's Llama to 'frontier model' performance The company also demonstrates initial training of a one-trillion-parameter AI model on a single machine using conventional DDR5 memory chips. https://www.zdnet.com/article/how-cerebras-boosted-metas-llama-to-frontier-model-performance/
Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, Yuandong Tian, 9 Dec 2024, Training Large Language Models to Reason in a Continuous Latent Space, https://arxiv.org/abs/2412.06769
Ben Dickson, December 10, 2024, OpenAI’s o1 model doesn’t show its thinking, giving open source an advantage, https://venturebeat.com/ai/heres-how-openai-o1-might-lose-ground-to-open-source-models/
Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, Lixin Gu, Xuehui Wang, Qingyun Li, Yimin Ren, Zixuan Chen, Jiapeng Luo, Jiahao Wang, Tan Jiang, Bo Wang, Conghui He, Botian Shi, Xingcheng Zhang, Han Lv, Yi Wang, Wenqi Shao, Pei Chu, Zhongying Tu, Tong He, Zhiyong Wu, Huipeng Deng, Jiaye Ge, Kai Chen, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, Wenhai Wang, 6 Dec 2024, Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling, https://arxiv.org/abs/2412.05271
Jiaqi Zhang, Chen Gao, Liyuan Zhang, Yong Li, Hongzhi Yin, 10 Dec 2024, SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World, https://arxiv.org/abs/2412.07472 https://github.com/tsinghua-fib-lab/SmartAgent
Kyle Wiggers, December 14, 2024, ‘Reasoning’ AI models have become a trend, for better or worse, https://techcrunch.com/2024/12/14/reasoning-ai-models-have-become-a-trend-for-better-or-worse/
Alberto Romero, Dec 21, 2024, OpenAI o3 Model Is a Message From the Future: Update All You Think You Know About AI. Incredible, a miracle, more than just a better state-of-the-art AI model. https://www.thealgorithmicbridge.com/p/openai-o3-model-is-a-message-from
Sabrina Ortiz, Dec. 20, 2024, OpenAI unveils its most advanced o3 reasoning model on its last day of 'shipmas', https://www.zdnet.com/article/openai-unveils-its-most-advanced-o3-reasoning-model-on-its-last-day-of-shipmas/
Tyler McDonald, Anthony Colosimo, Yifeng Li, Ali Emami, 2 Dec 2024, Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index, https://arxiv.org/abs/2412.01690
Jiaxiang Liu, Yuan Wang, Jiawei Du, Joey Tianyi Zhou, Zuozhu Liu, 18 Dec 2024, MedCoT: Medical Chain of Thought via Hierarchical Expert, https://arxiv.org/abs/2412.13736
Changyue Wang, Weihang Su, Qingyao Ai, Yiqun Liu, 23 Dec 2024, Knowledge Editing through Chain-of-Thought, https://arxiv.org/abs/2412.17727 https://github.com/bebr2/EditCoT
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan, 3 Dec 2023 (v2), Tree of Thoughts: Deliberate Problem Solving with Large Language Models, https://arxiv.org/abs/2305.10601 Code: https://github.com/princeton-nlp/tree-of-thought-llm
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, 10 Jan 2023 (v6), Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. https://arxiv.org/abs/2201.11903
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa, 29 Jan 2023 (v4), Large Language Models are Zero-Shot Reasoners, https://arxiv.org/abs/2205.11916 https://github.com/kojima-takeshi188/zero_shot_cot ("Let's think step by step" prepended to every prompt for a type of zero-shot CoT.)
Xuezhi Wang, Denny Zhou, 23 May 2024 (v2), Chain-of-Thought Reasoning Without Prompting, https://arxiv.org/abs/2402.10200 ("CoT decoding" is examining the alternative paths in the decoding algorithm, which is somewhat similar to Chain-of-Thought reasoning.)
xjdr-alt, Dec 2024, entropix: Entropy Based Sampling and Parallel CoT Decoding, https://github.com/xjdr-alt/entropix (Parallel decoding attempts to get something similar to CoT.)
Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, Dacheng Tao, 24 Dec 2024, Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search, https://arxiv.org/abs/2412.18319 https://github.com/HJYao00/Mulberry (Multimodal multi-step reasoning like CoT.)
Xiangjue Dong, Maria Teleki, James Caverlee, 18 Dec 2024, A Survey on LLM Inference-Time Self-Improvement, https://arxiv.org/abs/2412.14352 https://github.com/dongxiangjue/Awesome-LLM-Self-Improvement (Broad survey of reasoning improvement methods from multi-step inference to RALM to decoding algorithms.)
Jiaan Wang, Fandong Meng, Yunlong Liang, Jie Zhou, 23 Dec 2024, DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought, https://arxiv.org/abs/2412.17498 https://github.com/krystalan/DRT-o1 (Examines similes and metaphors in literature using long CoT.)
Jiacheng Ye, Shansan Gong, Liheng Chen, Lin Zheng, Jiahui Gao, Han Shi, Chuan Wu, Xin Jiang, Zhenguo Li, Wei Bi, Lingpeng Kong, 5 Dec 2024 (v3), Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models, https://arxiv.org/abs/2402.07754
Shiv Sakhuja, 25 Sep 2024, Chain-of-Thought (CoT) Prompting Explained: 7 Techniques for Optimizing AI Performance, https://hub.athina.ai/athina-originals/guides-chain-of-thought-cot-prompting-explained-7-techniques-for-optimizing-ai-performance/
Aryasomayajula Ram Bharadwaj, 5 Dec 2024, Understanding Hidden Computations in Chain-of-Thought Reasoning, https://arxiv.org/abs/2412.04537
Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, Thomas Back, 16 Jul 2024, Reasoning with Large Language Models, a Survey, https://arxiv.org/abs/2407.11511
Cheng Yang, Chufan Shi, Siheng Li, Bo Shui, Yujiu Yang, Wai Lam, 29 Dec 2024, LLM2: Let Large Language Models Harness System 2 Reasoning, https://arxiv.org/abs/2412.20372
Mayi Xu, Yunfeng Ning, Yongqi Li, Jianhao Chen, Jintao Wen, Yao Xiao, Shen Zhou, Birong Pan, Zepeng Bao, Xin Miao, Hankun Kang, Ke Sun, Tieyun Qian, 2 Jan 2025, Reasoning based on symbolic and parametric knowledge bases: a survey, https://arxiv.org/abs/2501.01030 (Extensive survey of reasoning from CoT to knowledge graphs to table-based reasoning.)
Yixin Ji, Juntao Li, Hai Ye, Kaixin Wu, Jia Xu, Linjian Mo, Min Zhang, 5 Jan 2025, Test-time Computing: from System-1 Thinking to System-2 Thinking, https://arxiv.org/abs/2501.02497
Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, Nathan Lile, Dakota Mahan, Louis Castricato, Jan-Philipp Franken, Nick Haber, Chelsea Finn, 8 Jan 2025, Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought, https://arxiv.org/abs/2501.04682
Andrea Matarazzo, Riccardo Torlone, 3 Jan 2025, A Survey on Large Language Models with some Insights on their Capabilities and Limitations, https://arxiv.org/abs/2501.04040 (Broad survey with many LLM topics covered from history to architectures to optimizations.)
Ziyang Ma, Zhuo Chen, Yuping Wang, Eng Siong Chng, Xie Chen, 13 Jan 2025, Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model, https://arxiv.org/abs/2501.07246
Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
G Bao, H Zhang, C Wang, L Yang, Y Zhang, Jan 2025, How Likely Do LLMs with CoT Mimic Human Reasoning? Proceedings of the 31st International Conference on Computational Linguistics, pages 7831–7850, January 19–24, 2025, https://aclanthology.org/2025.coling-main.524.pdf
Son, M., Won, Y.-J., & Lee, S. (2025). Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques. Applied Sciences, 15(3), 1430. https://doi.org/10.3390/app15031430 https://www.mdpi.com/2076-3417/15/3/1430
Manish Sanwal, 3 Feb 2025 (v2), Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models, https://arxiv.org/abs/2501.18645
Jianfeng Pan, Senyou Deng, Shaomang Huang, 4 Feb 2025, CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning, https://arxiv.org/abs/2502.02390 (Integrating results from an "associative memory" in CoT reasoning paths at inference time.)
Avinash Patil, 5 Feb 2025, Advancing Reasoning in Large Language Models: Promising Methods and Approaches, https://arxiv.org/abs/2502.03671
Daniel Fleischer, Moshe Berchansky, Gad Markovits, Moshe Wasserblat, 13 Feb 2025, SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models, https://arxiv.org/abs/2502.09390 https://github.com/IntelLabs/RAG-FiT/tree/square
Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H.S. Torr, Salman Khan, Fahad Shahbaz Khan, 28 Feb 2025, LLM Post-Training: A Deep Dive into Reasoning Large Language Models, https://arxiv.org/abs/2502.21321 https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
Bin Hong, Jiayu Liu, Zhenya Huang, Kai Zhang, Mengdi Zhang, 13 Aug 2025, Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization, https://arxiv.org/abs/2508.10164
Ke Niu, Haiyang Yu, Zhuofan Chen, Mengyang Zhao, Teng Fu, Bin Li, Xiangyang Xue, 13 Aug 2025, From Intent to Execution: Multimodal Chain-of-Thought Reinforcement Learning for Precise CAD Code Generation, https://arxiv.org/abs/2508.10118
Ziyu Guo, Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Rui Huang, Haoquan Zhang, Manyuan Zhang, Jiaming Liu, Shanghang Zhang, Peng Gao, Hongsheng Li, Pheng-Ann Heng, 23 Jul 2025, Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step, https://arxiv.org/abs/2501.13926
Ang Li, Charles Wang, Kaiyu Yue, Zikui Cai, Ollie Liu, Deqing Fu, Peng Guo, Wang Bill Zhu, Vatsal Sharan, Robin Jia, Willie Neiswanger, Furong Huang, Tom Goldstein, Micah Goldblum, 22 Jul 2025, Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning, https://arxiv.org/abs/2507.16746
Hulayyil Alshammari, Praveen Rao, 23 Jul 2025, Evaluating the Performance of AI Text Detectors, Few-Shot and Chain-of-Thought Prompting Using DeepSeek Generated Text, https://arxiv.org/abs/2507.17944
Binbin Ji, Siddharth Agrawal, Qiance Tang, and Yvonne Wu, 6 Jul 2025, Enhancing Spatial Reasoning in Vision-Language Models via Chain-of-Thought Prompting and Reinforcement Learning, https://arxiv.org/abs/2507.13362
Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, Wanxiang Che, 18 Jul 2025, Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models, https://arxiv.org/abs/2503.09567
Lei Chen, Xuanle Zhao, Zhixiong Zeng, Jing Huang, Yufeng Zhong, Lin Ma, 21 Jul 2025, Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner, https://arxiv.org/abs/2507.15509
Luyi Ma, Wanjia Zhang, Kai Zhao, Abhishek Kulkarni, Lalitesh Morishetti, Anjana Ganesh, Ashish Ranjan, Aashika Padmanabhan, Jianpeng Xu, Jason Cho, Praveen Kanumala, Kaushiki Nag, Sumit Dutta, Kamiya Motwani, Malay Patel, Evren Korpeoglu, Sushant Kumar, Kannan Achan, 19 Jul 2025, GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization, https://arxiv.org/abs/2507.14758
Hao Yang, Qinghua Zhao, Lei Li, 28 Jul 2025, How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation, https://arxiv.org/abs/2507.20758
Eunkyu Park, Wesley Hanwen Deng, Gunhee Kim, Motahhare Eslami, Maarten Sap, 27 Jul 2025, Cognitive Chain-of-Thought: Structured Multimodal Reasoning about Social Situations, https://arxiv.org/abs/2507.20409
Xiangning Yu, Zhuohan Wang, Linyi Yang, Haoxuan Li, Anjie Liu, Xiao Xue, Jun Wang, Mengyue Yang, 26 Jul 2025, Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning, https://arxiv.org/abs/2506.09853
Ping Yu, Jack Lanchantin, Tianlu Wang, Weizhe Yuan, Olga Golovneva, Ilia Kulikov, Sainbayar Sukhbaatar, Jason Weston, Jing Xu, 31 Jul 2025, CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks, https://arxiv.org/abs/2507.23751
Xi Chen, Aske Plaat, Niki van Stein, 24 Jul 2025, How does Chain of Thought Think? Mechanistic Interpretability of Chain-of-Thought Reasoning with Sparse Autoencoding, https://arxiv.org/abs/2507.22928
Shixin Yi, Lin Shang, 1 Aug 2025, CoRGI: Verified Chain-of-Thought Reasoning with Visual Grounding, https://arxiv.org/abs/2508.00378
Jianwei Wang, Ziming Wu, Fuming Lai, Shaobing Lian, Ziqian Zeng, 1 Aug 2025, SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought, https://arxiv.org/abs/2508.00574
Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu, 2 Aug 2025, Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens, https://arxiv.org/abs/2508.01191
Jialiang Hong, Taihang Zhen, Kai Chen, Jiaheng Liu, Wenpeng Zhu, Jing Huo, Yang Gao, Depeng Wang, Haitao Wan, Xi Yang, Boyan Wang, Fanyu Meng, 4 Aug 2025, Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning, https://arxiv.org/abs/2508.02178
Chloe Li, Mary Phuong, Noah Y. Siegel, 31 Jul 2025, LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring, https://arxiv.org/abs/2508.00943
Weibo Zhou, Lingbo Li, Shangsong Liang, 2 Aug 2025, D-SCoRE: Document-Centric Segmentation and CoT Reasoning with Structured Export for QA-CoT Data Generation, https://arxiv.org/abs/2508.01309
Fan Gao, Cheng Huang, Nyima Tashi, Yutong Liu, Xiangxiang Wang, Thupten Tsering, Ban Ma-bao, Renzeg Duojie, Gadeng Luosang, Rinchen Dongrub, Dorje Tashi, Xiao Feng, Hao Wang, Yongbin Yu, 4 Aug 2025, TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models, https://arxiv.org/abs/2508.01977
Huihan Li, You Chen, Siyuan Wang, Yixin He, Ninareh Mehrabi, Rahul Gupta, Xiang Ren, 4 Aug 2025, Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time, https://arxiv.org/abs/2508.02037
Hongbo Jin, Ruyang Liu, Wenhao Zhang, Guibo Luo, Ge Li, 3 Aug 2025, CoT-Vid: Dynamic Chain-of-Thought Routing with Self Verification for Training-Free Video Reasoning, https://arxiv.org/abs/2505.11830
Zeju Li, Jianyuan Zhong, Ziyang Zheng, Xiangyu Wen, Zhijian Xu, Yingying Cheng, Fan Zhang, Qiang Xu, 5 Aug 2025, Compressing Chain-of-Thought in LLMs via Step Entropy, https://arxiv.org/abs/2508.03346
Jueon Park, Yein Park, Minju Song, Soyon Park, Donghyeon Lee, Seungheun Baek and Jaewoo Kang, 5 Aug 2025, CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction, https://arxiv.org/abs/2508.03159
Junyao Yang, Jianwei Wang, Huiping Zhuang, Cen Chen, Ziqian Zeng, 5 Aug 2025, RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior, https://arxiv.org/abs/2508.03140
Weihua Zheng, Xin Huang, Zhengyuan Liu, Tarun Kumar Vangani, Bowei Zou, Xiyan Tao, Yuhao Wu, Ai Ti Aw, Nancy F. Chen, Roy Ka-Wei Lee, 5 Aug 2025, AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought, https://arxiv.org/abs/2501.16154
Xingyu Chen, Junxiu An, Jun Guo, Li Wang, Jingcai Guo, 6 Aug 2025, KG-Augmented Executable CoT for Mathematical Coding, https://arxiv.org/abs/2508.04072
Xiao Wang, Liye Jin, Xufeng Lou, Shiao Wang, Lan Chen, Bo Jiang, Zhipeng Zhang, 7 Aug 2025, ReasoningTrack: Chain-of-Thought Reasoning for Long-term Vision-Language Tracking, https://arxiv.org/abs/2508.05221
Haonan Shangguan, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, and Ge Yu, 7 Aug 2025, Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation, https://arxiv.org/abs/2508.05234
Tianyun Yang, Yunwen Li, Ziniu Li, Zhihang Lin, Ruoyu Sun, Tian Ding, 12 Aug 2025, Bridging Formal Language with Chain-of-Thought Reasoning to Geometry Problem Solving, https://arxiv.org/abs/2508.09099
Haiyun Guo, ZhiYan Hou, Yu Chen, Jinghan He, Yandu Sun, Yuzhe Zhou, Shujing Guo, Kuan Zhu, Jinqiao Wang, 31 Jul 2025, MLLM-CBench:A Comprehensive Benchmark for Continual Instruction Tuning of Multimodal LLMs with Chain-of-Thought Reasoning Analysis, https://arxiv.org/abs/2508.08275
Axel Delaval, Shujian Yang, Haicheng Wang, Han Qiu, Jialiang Lu, 15 Aug 2025, ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection, https://arxiv.org/abs/2508.11281
Phuong Minh Nguyen, Tien Huu Dang, Naoya Inoue, 17 Aug 2025, Non-Iterative Symbolic-Aided Chain-of-Thought for Logical Reasoning, https://arxiv.org/abs/2508.12425
Zhifeng Kong, Arushi Goel, Joao Felipe Santos, Sreyan Ghosh, Rafael Valle, Wei Ping, Bryan Catanzaro, 15 Aug 2025, Audio Flamingo Sound-CoT Technical Report: Improving Chain-of-Thought Reasoning in Sound Understanding, https://arxiv.org/abs/2508.11818
Ruheng Wang, Hang Zhang, Trieu Nguyen, Shasha Feng, Hao-Wei Pang, Xiang Yu, Li Xiao, Peter Zhiping Zhang, 20 Aug 2025, PepThink-R1: LLM for Interpretable Cyclic Peptide Optimization with CoT SFT and Reinforcement Learning, https://arxiv.org/abs/2508.14765
Josh Barua, Seun Eisape, Kayo Yin, Alane Suhr, 20 Aug 2025, Long Chain-of-Thought Reasoning Across Languages, https://arxiv.org/abs/2508.14828
Wenqiao Zhu, Ji Liu, Rongjuncheng Zhang, Haipang Wu, Yulun Zhang, 21 Aug 2025, CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning, https://arxiv.org/abs/2508.15868

Tree-of-Thought (ToT)

Research papers on Tree-of-Thought (ToT) prompting:

Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan, 3 Dec 2023 (v2), Tree of Thoughts: Deliberate Problem Solving with Large Language Models, https://arxiv.org/abs/2305.10601 Code: https://github.com/princeton-nlp/tree-of-thought-llm
Antonis Iliakis, Jul 5, 2024, Amazing Chat GPT Prompts That Will Take You to The Next Level — Part 3, https://generativeai.pub/i-asked-chat-gpt-to-think-like-a-human-heres-what-i-found-out-7a6017109d66
Alan Boyle, Isha Gupta, Sebastian Hönig, Lukas Mautner, Kenza Amara, Furui Cheng, Mennatallah El-Assady, 31 Aug 2024, iToT: An Interactive System for Customized Tree-of-Thought Generation, https://arxiv.org/abs/2409.00413
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha, 5 Feb 2024, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, https://arxiv.org/abs/2402.07927
Emile J, May 28, 2023, Tree of Thoughts (ToT) Prompting: The Basics, https://medium.com/@emile.jonkers/tree-of-thought-tot-prompting-simply-explained-dca7e719752
Qiqi Chen, Xinpeng Wang, Philipp Mondorf, Michael A. Hedderich, Barbara Plank, 24 Oct 2024 (v2), Understanding When Tree of Thoughts Succeeds: Larger Models Excel in Generation, Not Discrimination, https://arxiv.org/abs/2410.17820 http://github.com/mainlp/tot-eval
Cameron R. Wolfe, Dec 23, 2023, Tree of Thoughts Prompting. Solving multi-step problems with LLMs via deliberate planning and exploration, https://towardsdatascience.com/tree-of-thoughts-prompting-65a3e51f9ac4
Cameron R. Wolfe, Aug 21, 2023, Tree of Thoughts Prompting. Solving multi-step problems with LLMs via deliberate planning and exploration, https://cameronrwolfe.substack.com/p/tree-of-thoughts-prompting
Tyler McDonald, Anthony Colosimo, Yifeng Li, Ali Emami, 2 Dec 2024, Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index, https://arxiv.org/abs/2412.01690
Shiv Sakhuja, 25 Sep 2024, Chain-of-Thought (CoT) Prompting Explained: 7 Techniques for Optimizing AI Performance, https://hub.athina.ai/athina-originals/guides-chain-of-thought-cot-prompting-explained-7-techniques-for-optimizing-ai-performance/
Changcheng Li, Xiangyu Wang, Qiuju Chen, Xiren Zhou, Huanhuan Chen, 5 Dec 2024, MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM, https://arxiv.org/abs/2412.03987
Maciej Besta, Julia Barth, Eric Schreiber, Ales Kubicek, Afonso Catarino, Robert Gerstenberger, Piotr Nyczyk, Patrick Iff, Yueling Li, Sam Houliston, Tomasz Sternal, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Łukasz Flis, Hannes Eberhard, Hubert Niewiadomski, Torsten Hoefler, 23 Jan 2025 (v3), Reasoning Language Models: A Blueprint, https://arxiv.org/abs/2501.11223 (Survey and blueprint for how to build a Large Reasoning Model.)
G Wang, S Zhang, T Zhan, Z Shen, J Li, X Hu, X Sun, Jan 2025, Unlocking the Mysteries of OpenAI o1: A Survey of the Reasoning Abilities of Large Language Models, https://openreview.net/pdf?id=J0ADLa2rNp
Son, M., Won, Y.-J., & Lee, S. (2025). Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques. Applied Sciences, 15(3), 1430. https://doi.org/10.3390/app15031430 https://www.mdpi.com/2076-3417/15/3/1430
Avinash Patil, 5 Feb 2025, Advancing Reasoning in Large Language Models: Promising Methods and Approaches, https://arxiv.org/abs/2502.03671
Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao, 27 Feb 2025 (v2), Dynamic Parallel Tree Search for Efficient LLM Reasoning, https://arxiv.org/abs/2502.16235
Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H.S. Torr, Salman Khan, Fahad Shahbaz Khan, 28 Feb 2025, LLM Post-Training: A Deep Dive into Reasoning Large Language Models, https://arxiv.org/abs/2502.21321 https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
Ruiyan Qi, Congding Wen, Weibo Zhou, Shangsong Liang, Lingbo Li, 15 Aug 2025, LETToT: Label-Free Evaluation of Large Language Models On Tourism Using Expert Tree-of-Thought, https://arxiv.org/abs/2508.11280

Skeleton-of-Thought

Skeleton-of-thought is a technique that aims not only to improve accuracy, but also to improve speed and cost efficiency of inference by splitting a single prompt into multiple, smaller sub-prompts. These can be executed in parallel, to reduce overall latency.

The basic speedup works like this:

Generate an outline quickly (short LLM answer)
For each outline point, generate a brief answer (multiple focused LLM queries to compute short answers in parallel)
Combine them into a final, longer answer (possibly with an LLM, but this will be a long text, so heuristic packing/merging of sub-answers is faster)

Research papers on skeleton-of-thought:

L. Zheng, L. Yin, Z. Xie, J. Huang, C. Sun, C. H. Yu, S. Cao, C. Kozyrakis, I. Stoica, J. E. Gonzalez et al., Dec 2023, Efficiently programming large language models using SGLang, arXiv preprint arXiv:2312.07104, 2023, https://arxiv.org/abs/2312.07104 (Uses a radix attention method, a trie or prefix tree, for KV caching.)
Xuefei Ning , Zinan Lin , November 17, 2023 Skeleton-of-Thought: Parallel decoding speeds up and improves LLM output, Microsoft Research Blog, https://www.microsoft.com/en-us/research/blog/skeleton-of-thought-parallel-decoding-speeds-up-and-improves-llm-output/ Code: https://github.com/imagination-research/sot/
S. Jin, Y. Wu, H. Zheng, Q. Zhang, M. Lentz, Z. M. Mao, A. Prakash, F. Qian, and D. Zhuo, “Adaptive skeleton graph decoding,” arXiv preprint arXiv:2402.12280, 2024. https://arxiv.org/abs/2402.12280
M. Liu, A. Zeng, B. Wang, P. Zhang, J. Tang, and Y. Dong, “Apar: Llms can do auto-parallel auto-regressive decoding,” arXiv preprint arXiv:2401.06761, 2024. https://arxiv.org/abs/2401.06761
8 Jun 2024 (v2), A Survey on Efficient Inference for Large Language Models, Zixuan Zhou, Xuefei Ning, Ke Hong, Tianyu Fu, Jiaming Xu, Shiyao Li, Yuming Lou, Luning Wang, Zhihang Yuan, Xiuhong Li, Shengen Yan, Guohao Dai, Xiao-Ping Zhang, Yuhan Dong, Yu Wang, https://arxiv.org/abs/2404.14294
Mahsa Khoshnoodi, Vinija Jain, Mingye Gao, Malavika Srikanth, Aman Chadha, 24 May 2024 (v2), A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models, https://arxiv.org/abs/2405.13019
Steven Kolawole, KeshavSanthanam, Virginia Smith, Pratiksha Thaker, Nov 2024, Extracting Parallelism from LargeLanguageModelQueries, https://openreview.net/pdf?id=CZHt9kLS5S
Huiyou Zhan, Xuan Zhang, Haisheng Tan, Han Tian, Dongping Yong, Junyang Zhang, Xiang-Yang Li, 16 Jan 2025, PICE: A Semantic-Driven Progressive Inference System for LLM Serving in Cloud-Edge Networks, https://arxiv.org/abs/2501.09367 (Generate an outline in the cloud that is filled in by edge models, which is similar to Skeleton-of-Thought.)
Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, Yu Wang, May 2024, Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation, ICLR 2024, https://www.microsoft.com/en-us/research/publication/skeleton-of-thought-large-language-models-can-do-parallel-decoding/ https://neurips2023-enlsp.github.io/papers/paper_33.pdf Code: https://github.com/imagination-research/sot/
Ruibin Xiong, Yimeng Chen, Dmitrii Khizbullin, Jürgen Schmidhuber, 11 Mar 2025, Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models, https://arxiv.org/abs/2503.08275
Yijiong Yu, 26 Mar 2025, Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence, https://arxiv.org/abs/2503.20533 https://github.com/yuyijiong/parallel-decoding-in-one-sequence
Siqi Fan, Peng Han, Shuo Shang, Yequan Wang, Aixin Sun, 28 May 2025, CoThink: Token-Efficient Reasoning via Instruct Models Guiding Reasoning Models, https://arxiv.org/abs/2505.22017 (Generate an outline before reasoning.)
Ali Ismail-Fawaz and Maxime Devanne and Stefano Berretti and Jonathan Weber and Germain Forestier, 28 Jul 2025, Deep Learning for Skeleton Based Human Motion Rehabilitation Assessment: A Benchmark, https://arxiv.org/abs/2507.21018
Tiantian Liu, Xiao Li, Huan Li, Hua Lu, Christian S. Jensen, Jianliang Xu, 4 Aug 2025, Skeleton-Guided Learning for Shortest Path Search, https://arxiv.org/abs/2508.02270
Youwei Zhou and Tianyang Xu and Cong Wu and Xiaojun Wu and Josef Kittler, 4 Aug 2025, Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connections, https://arxiv.org/abs/2411.14796
Devansh Arora, Nitin Kumar, Sukrit Gupta, 15 Aug 2025, Does the Skeleton-Recall Loss Really Work?, https://arxiv.org/abs/2508.11374

Prompt Optimization

Prompt optimization is the technique of improving the results from prompt engineering. In a sense, this covers the whole discipline, but there are specific technical approaches to optimizing prompts. One subarea is automatic prompt optimization, also called "programmatic prompting," which uses an LLM to automatically tweak prompt wording, but there are also many manual approaches.

Research papers on prompt optimization include:

Cameron R. Wolfe, Nov 04, 2024, Automatic Prompt Optimization. Practical techniques for improving prompt quality without manual effort, https://cameronrwolfe.substack.com/p/automatic-prompt-optimization
Akshay Nambi , Tanuja Ganu , December 17, 2024, PromptWizard: The future of prompt optimization through feedback-driven self-evolving prompts, https://www.microsoft.com/en-us/research/blog/promptwizard-the-future-of-prompt-optimization-through-feedback-driven-self-evolving-prompts/
Shuzheng Gao, Chaozheng Wang, Cuiyun Gao, Xiaoqian Jiao, Chun Yong Chong, Shan Gao, Michael Lyu, 2 Jan 2025, The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation, https://arxiv.org/abs/2501.01329
Wenxin Luo, Weirui Wang, Xiaopeng Li, Weibo Zhou, Pengyue Jia, Xiangyu Zhao, 12 Jan 2025, TAPO: Task-Referenced Adaptation for Prompt Optimization, https://arxiv.org/abs/2501.06689
Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
Can Wang, Dianbo Sui, Bolin Zhang, Xiaoyu Liu, Jiabao Kang, Zhidong Qiao, Zhiying Tu, Jan 2025, A Framework for Effective Invocation Methods of Various LLM Services, Proceedings of the 31st International Conference on Computational Linguistics, pages 6953–6965, January 19–24, 2025, Association for Computational Linguistics, https://aclanthology.org/2025.coling-main.464.pdf
Yuanheng Fang, Guoqing Chao, Wenqiang Lei, Shaobo Li, Dianhui Chu, 21 Jan 2025, CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning, https://arxiv.org/abs/2501.12226 (CoT with integration of clustering and prompt optimization techniques.)
Krish Maniar and William Fu-Hinthorn, LangChain, Jan 28, 2025, Exploring Prompt Optimization, https://blog.langchain.dev/exploring-prompt-optimization/ (Long article evaluating various LLMs for automatic prompt optimization.)
Mingze Kong, Zhiyong Wang, Yao Shu, Zhongxiang Dai, 2 Feb 2025, Meta-Prompt Optimization for LLM-Based Sequential Decision Making, https://arxiv.org/abs/2502.00728
Jinyu Xiang, Jiayi Zhang, Zhaoyang Yu, Fengwei Teng, Jinhao Tu, Xinbing Liang, Sirui Hong, Chenglin Wu, Yuyu Luo, 7 Feb 2025. Self-Supervised Prompt Optimization, https://arxiv.org/abs/2502.06855 https://github.com/geekan/MetaGPT
Yupeng Chang, Yi Chang, Yuan Wu, 20 Feb 2025, Transfer-Prompting: Enhancing Cross-Task Adaptation in Large Language Models via Dual-Stage Prompts Optimization, https://arxiv.org/abs/2502.14211 https://github.com/llm172/Transfer-Prompting
Rin Ashizawa, Yoichi Hirose, Nozomu Yoshinari, Kento Uchida, Shinichi Shirakawa, 3 Mar 2025, Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers, https://arxiv.org/abs/2503.01163 https://github.com/shiralab/OPTS
Leixian Shen, Haotian Li, Yifang Wang, Xing Xie, Huamin Qu, 4 Mar 2025, Prompting Generative AI with Interaction-Augmented Instructions, https://arxiv.org/abs/2503.02874
Thilak Shekhar Shriyan, Janavi Srinivasan, Suhail Ahmed, Richa Sharma, Arti Arya, March 2025, SwarmPrompt: Swarm Intelligence-Driven Prompt Optimization Using Large Language Models, Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025), Volume 3, pages 86-93, https://www.scitepress.org/Papers/2025/130903/130903.pdf
Dengyun Peng, Yuhang Zhou, Qiguang Chen, Jinhao Liu, Jingjing Chen, Libo Qin, 19 Mar 2025 (v3), DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective, https://arxiv.org/abs/2503.13413 https://github.com/sfasfaffa/DLPO
Vagner Figueredo de Santana, Sara Berger, Tiago Machado, Maysa Malfiza Garcia de Macedo, Cassia Sampaio Sanctos, Lemara Williams, and Zhaoqing Wu. 2025. Can LLMs Recommend More Responsible Prompts? In Proceedings of the 30th International Conference on Intelligent User Interfaces (IUI '25). Association for Computing Machinery, New York, NY, USA, 298–313. https://doi.org/10.1145/3708359.3712137 https://dl.acm.org/doi/full/10.1145/3708359.3712137 https://dl.acm.org/doi/pdf/10.1145/3708359.3712137
Jiale Cheng, Ruiliang Lyu, Xiaotao Gu, Xiao Liu, Jiazheng Xu, Yida Lu, Jiayan Teng, Zhuoyi Yang, Yuxiao Dong, Jie Tang, Hongning Wang, Minlie Huang, 26 Mar 2025, VPO: Aligning Text-to-Video Generation Models with Prompt Optimization, https://arxiv.org/abs/2503.20491
Jian Zhang, Zhangqi Wang, Haiping Zhu, Jun Liu, Qika Lin, Erik Cambria, 21 Mar 2025, MARS: A Multi-Agent Framework Incorporating Socratic Guidance for Automated Prompt Optimization, https://arxiv.org/abs/2503.16874
AD Al Hauna, AP Yunus, M Fukui, S Khomsah - International Journal on Robotics, Apr 2025, Enhancing LLM Efficiency: A Literature Review of Emerging Prompt Optimization Strategies, https://doi.org/10.33093/ijoras.2025.7.1.9 https://mmupress.com/index.php/ijoras/article/view/1311 PDF: https://mmupress.com/index.php/ijoras/article/view/1311/834
Ximing Dong, Shaowei Wang, Dayi Lin, Ahmed E. Hassan, 15 May 2025, Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization, https://arxiv.org/abs/2505.10736
Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, Mak Lee Onn, Gee Wah Ng, Kezhi Mao, 20 May 2025 (v2), Rethinking Prompt Optimizers: From Prompt Merits to Optimization, https://arxiv.org/abs/2505.09930 https://github.com/MidiyaZhu/MePO
Yumin Choi, Jinheon Baek, Sung Ju Hwang, 14 May 2025, System Prompt Optimization with Meta-Learning, https://arxiv.org/abs/2505.09666
Ziyu Zhou, Yihang Wu, Jingyuan Yang, Zhan Xiao, Rongjun Li, 13 May 2025, Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow, https://arxiv.org/abs/2505.08303
Chun-Pai Yang, Kan Zheng, Shou-De Lin, 11 May 2025, PLHF: Prompt Optimization with Few-Shot Human Feedback, https://arxiv.org/abs/2505.07886
Kwesi Cobbina, Tianyi Zhou, 30 Jul 2025, Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning, https://arxiv.org/abs/2507.22887 (Best place to put examples is at the start of a prompt not the end.)
Ye Yu, Yaoning Yu, Haohan Wang, 12 Jun 2025, PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models, https://arxiv.org/abs/2506.10716
Can Yang, Bernardo Pereira Nunes, and Sergio Rodríguez Méndez. 2025. LLM as Auto-Prompt Engineer: Automated NER Prompt Optimisation. In Companion Proceedings of the ACM on Web Conference 2025 (WWW '25). Association for Computing Machinery, New York, NY, USA, 2574–2578. https://doi.org/10.1145/3701716.3717818 https://dl.acm.org/doi/abs/10.1145/3701716.3717818 https://dl.acm.org/doi/pdf/10.1145/3701716.3717818
Po-Cheng Hsieh, Wei-Po Lee, 29 April 2025, A Particle Swarm Optimization-Based Approach Coupled With Large Language Models for Prompt Optimization, https://doi.org/10.1111/exsy.70049 https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.70049
Tom Zehle, Moritz Schlager, Timo Heiß, Matthias Feurer, 17 Jun 2025 (v4), CAPO: Cost-Aware Prompt Optimization, https://arxiv.org/abs/2504.16005
Saurabh Srivastava, Ziyu Yao, 10 Apr 2025, Revisiting Prompt Optimization with Large Reasoning Models-A Case Study on Event Extraction, https://arxiv.org/abs/2504.07357?
Wenliang Zheng, Sarkar Snigdha Sarathi Das, Yusen Zhang, Rui Zhang, 4 Apr 2025, GREATERPROMPT: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization, https://arxiv.org/abs/2504.03975 https://github.com/psunlpgroup/GreaterPrompt
Claudio Spiess, Mandana Vaziri, Louis Mandel, Martin Hirzel, 23 Jun 2025 (v2), AutoPDL: Automatic Prompt Optimization for LLM Agents, https://arxiv.org/abs/2504.04365
Anoop Kotha, Julian Lee, Eric Zakariasson, Anoop Kotha, Julian Lee, OpenAI, Aug 2025, GPT-5 prompting guide, https://cookbook.openai.com/examples/gpt-5/gpt-5_prompting_guide
Saaduddin Mahmud, Mason Nakamura, Kyle H. Wray, Shlomo Zilberstein, 8 Aug 2025, Inference-Aware Prompt Optimization for Aligning Black-Box Large Language Models, https://arxiv.org/abs/2508.10030
Rithesh Murthy, Ming Zhu, Liangwei Yang, Jielin Qiu, Juntao Tan, Shelby Heinecke, Caiming Xiong, Silvio Savarese, Huan Wang, 22 Jul 2025, Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models, https://arxiv.org/abs/2507.14241
Andreea Nica, Ivan Zakazov, Nicolas Mario Baldwin, Saibo Geng, Robert West, 24 Jul 2025, TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards, https://arxiv.org/abs/2507.18618
Nandana Mihindukulasooriya, Niharika S. D'Souza, Faisal Chowdhury, Horst Samulowitz, 4 Aug 2025, Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study, https://arxiv.org/abs/2506.19773
Yao Wu, 16 Aug 2025, J6: Jacobian-Driven Role Attribution for Multi-Objective Prompt Optimization in LLMs, https://arxiv.org/abs/2508.12086
Anas Mohamed, Azal Ahmad Khan, Xinran Wang, Ahmad Faraz Khan, Shuwen Ge, Saman Bahzad Khan, Ayaan Ahmad, Ali Anwar, 29 Jul 2025, Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering, https://arxiv.org/abs/2507.20133
Fei Xu Yu, Gina Adam, Nathaniel D. Bastian, Tian Lan, 8 Aug 2025, Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization, https://arxiv.org/abs/2508.05995
Artem Chernodub and Aman Saini and Yejin Huh and Vivek Kulkarni and Vipul Raheja, 12 Aug 2025, APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification, https://arxiv.org/abs/2508.09378
Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang, Pan Zhou, Lichao Sun, Neil Zhenqiang Gong, 24 Aug 2025, Optimization-based Prompt Injection Attack to LLM-as-a-Judge, https://arxiv.org/abs/2403.17710

Programmatic Prompt Engineering

Programmatic prompting or "auto prompting" is the use of software automation, such as an extra LLM step, to auto-create better prompts for users based on their original query text. The results should be better prompt structures and better answers.

Research on programmatic prompt engineering:

Asankhaya Sharma (codelion), Sep 2024, Optillm: Optimizing inference proxy for LLMs, https://github.com/codelion/optillm
Anthropic, 2024, Automatically generate first draft prompt templates, https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/prompt-generator
David Gewirtz, April 19, 2024, Logitech's free AI Prompt Builder is surprisingly handy, https://www.zdnet.com/article/logitechs-free-ai-prompt-builder-is-surprisingly-handy-heres-how-im-using-it/
Julian Yip, May 6, 2024, Prompt Like a Data Scientist: Auto Prompt Optimization and Testing with DSPy: Applying machine learning methodology to prompt building, Towards Data Science, https://towardsdatascience.com/prompt-like-a-data-scientist-auto-prompt-optimization-and-testing-with-dspy-ff699f030cb7
Anthropic, 21 May 2024, Generate better prompts in the developer console, https://www.anthropic.com/news/prompt-generator
Heiko Hotz, Sep 2024, Automated Prompt Engineering: The Definitive Hands-On Guide, https://towardsdatascience.com/automated-prompt-engineering-the-definitive-hands-on-guide-1476c8cd3c50
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha, 5 Feb 2024, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, https://arxiv.org/abs/2402.07927
Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola, 7 Oct 2022, Automatic Chain of Thought Prompting in Large Language Models, https://arxiv.org/abs/2210.03493 https://github.com/amazon-research/auto-cot
Rinon Gal, Adi Haviv, Yuval Alaluf, Amit H. Bermano, Daniel Cohen-Or, Gal Chechik, 2 Oct 2024, ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation, https://arxiv.org/abs/2410.01731 https://comfygen-paper.github.io/
Jose Antonio Lanz, Oct 15, 2024, OpenAI Unveils Secret Meta Prompt—And It’s Very Different From Anthropic's Approach, Under the hood, OpenAI’s structured approach to prompt generation sets it apart from Anthropic’s human-like, chatbot approach. https://decrypt.co/285854/openai-secret-meta-prompt-anthropic
Jordan Gibbs, Oct 2024, Become a Master Prompt Engineer with ChatGPT Canvas. Write perfect prompts in a fraction of the time with my new GPT Canvas workflow. https://medium.com/@jordan_gibbs/become-a-master-prompt-engineer-with-chatgpt-canvas-c4eebb8e4c23
Cameron R. Wolfe, Nov 04, 2024, Automatic Prompt Optimization. Practical techniques for improving prompt quality without manual effort, https://cameronrwolfe.substack.com/p/automatic-prompt-optimization
Manpreet Singh, Oct 31, 2024, Let's Simplifying How We Talk to AI Using Prompt Declaration Language (PDL), https://pub.towardsai.net/lets-simplifying-how-we-talk-to-ai-using-prompt-declaration-language-pdl-b1824c4de833
Ian Drosos, Jack Williams, Advait Sarkar, Nicholas Wilson, 3 Dec 2024, Dynamic Prompt Middleware: Contextual Prompt Refinement Controls for Comprehension Tasks, https://arxiv.org/abs/2412.02357
Stephen MacNeil, Andrew Tran, Joanne Kim, Ziheng Huang, Seth Bernstein, Dan Mogil, 3 Jul 2023, Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances, https://arxiv.org/abs/2307.01142
Akshay Nambi , Tanuja Ganu , December 17, 2024, PromptWizard: The future of prompt optimization through feedback-driven self-evolving prompts, https://www.microsoft.com/en-us/research/blog/promptwizard-the-future-of-prompt-optimization-through-feedback-driven-self-evolving-prompts/
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, Jimmy Ba, 10 Mar 2023 (v2), Large Language Models Are Human-Level Prompt Engineers, https://arxiv.org/abs/2211.01910
Daan Kepel, Konstantina Valogianni, 25 Jun 2024, Autonomous Prompt Engineering in Large Language Models, https://arxiv.org/abs/2407.11000
Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, Zhiting Hu, 7 Dec 2023 (v2), PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization, https://arxiv.org/abs/2310.16427 https://github.com/XinyuanWangCS/PromptAgent
Muhayy Ud Din, Jan Rosell, Waseem Akram, Isiah Zaplana, Maximo A Roa, Lakmal Seneviratne, Irfan Hussain, 10 Dec 2024, Ontology-driven Prompt Tuning for LLM-based Task and Motion Planning, https://arxiv.org/abs/2412.07493 https://muhayyuddin.github.io/llm-tamp/ (Detecting objects in the prompt text and then using a RALM algorithm to query an ontology database.)
Zeru Shi, Zhenting Wang, Yongye Su, Weidi Luo, Fan Yang, Yongfeng Zhang, 24 Dec 2024, Robustness-aware Automatic Prompt Optimization, https://arxiv.org/abs/2412.18196
Shuyang Wang, Somayeh Moazeni, Diego Klabjan, 7 Jan 2025, A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models, https://arxiv.org/abs/2501.03508
Onyedikachukwu Czar, Jan 2025, Four Creative Ways I use AI as a Content Writer. Collaborating with AI is how I attain an edge. https://medium.com/swlh/four-creative-ways-i-use-ai-as-a-content-writer-b31726f546de
Wenxin Luo, Weirui Wang, Xiaopeng Li, Weibo Zhou, Pengyue Jia, Xiangyu Zhao, 12 Jan 2025, TAPO: Task-Referenced Adaptation for Prompt Optimization, https://arxiv.org/abs/2501.06689
Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
Krish Maniar and William Fu-Hinthorn, LangChain, Jan 28, 2025, Exploring Prompt Optimization, https://blog.langchain.dev/exploring-prompt-optimization/ (Long article evaluating various LLMs for automatic prompt optimization.)
Li Yin, Zhangyang Wang, 30 Jan 2025 (v2), LLM-AutoDiff: Auto-Differentiate Any LLM Workflow, https://arxiv.org/abs/2501.16673
Mingze Kong, Zhiyong Wang, Yao Shu, Zhongxiang Dai, 2 Feb 2025, Meta-Prompt Optimization for LLM-Based Sequential Decision Making, https://arxiv.org/abs/2502.00728
Jae Yong Lee, Sungmin Kang, Shin Yoo, 31 Jan 2025, Can We Predict the Effect of Prompts? https://arxiv.org/abs/2501.18883
Bharani Subramaniam, 13 February 2025, Emerging Patterns in Building GenAI Products, https://martinfowler.com/articles/gen-ai-patterns/
Sam Lin, Wenyue Hua, Lingyao Li, Zhenting Wang, Yongfeng Zhang, 17 Feb 2025. ADO: Automatic Data Optimization for Inputs in LLM Prompts, https://arxiv.org/pdf/2502.11436 (Reformulating the input context such as by semantical marking of relevant content or formatting changes.)
Wenwu Li, Xiangfeng Wang, Wenhao Li, Bo Jin, 17 Feb 2025, A Survey of Automatic Prompt Engineering: An Optimization Perspective, https://arxiv.org/abs/2502.11560
Yupeng Chang, Yi Chang, Yuan Wu, 20 Feb 2025, Transfer-Prompting: Enhancing Cross-Task Adaptation in Large Language Models via Dual-Stage Prompts Optimization, https://arxiv.org/abs/2502.14211 https://github.com/llm172/Transfer-Prompting
Zihao Zeng, Xuyao Huang, Boxiu Li, Zhijie Deng, 19 Feb 2025, SIFT: Grounding LLM Reasoning in Contexts via Stickers, https://arxiv.org/abs/2502.14922 https://github.com/zhijie-group/SIFT (Multi-step reasoning where the LLM first generates a modified prompt that summarizes the key points, and then does inference for both the original and modified prompts, then comparing results and adjusting forwards and backwards.)
Yihang Yao, Zhepeng Cen, Miao Li, William Han, Yuyou Zhang, Emerson Liu, Zuxin Liu, Chuang Gan, Ding Zhao, 25 Feb 2025, Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training, https://arxiv.org/abs/2502.17800 (Using "query augmentation" as a type of automatic prompt optimization inside a reasoning chain.)
Sean Wu, Michael Koo, Fabien Scalzo, Ira Kurtz, 21 Feb 2025, AutoMedPrompt: A New Framework for Optimizing LLM Medical Prompts Using Textual Gradients, https://arxiv.org/abs/2502.15944
Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, Sricharan Kumar, 26 Feb 2025, Automatic Prompt Optimization via Heuristic Search: A Survey, https://arxiv.org/abs/2502.18746 (Survey of auto prompting, from basic LLM enhancements to some methods quite similar to RALM and TALM.)
Xiangyan Qu, Gaopeng Gou, Jiamin Zhuang, Jing Yu, Kun Song, Qihao Wang, Yili Li, Gang Xiong, 27 Feb 2025, ProAPO: Progressively Automatic Prompt Optimization for Visual Classification, https://arxiv.org/abs/2502.19844
Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal, 2023, GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3845–3864, Dubrovnik, Croatia. Association for Computational Linguistics, https://aclanthology.org/2023.eacl-main.277/
Gurusha Juneja, Gautam Jajoo, Nagarajan Natarajan, Hua Li, Jian Jiao, Amit Sharma, 19 May 2025 (v2), Task Facet Learning: A Structured Approach to Prompt Optimization, https://arxiv.org/abs/2406.10504 https://aka.ms/uniprompt
Bairu Hou, Joe O’connor, Jacob Andreas, Shiyu Chang, and Yang Zhang. 2023. Promptboosting: Black-box text classification with ten forward passes. In Inter national Conference on Machine Learning, pages 13309–13324. PMLR. https://arxiv.org/abs/2212.09257
Shuzheng Gao, Chaozheng Wang, Cuiyun Gao, Xiao qian Jiao, Chun Yong Chong, Shan Gao, and Michael Lyu. 2025. The prompt alchemist: Automated llm tailored prompt optimization for test case generation. arXiv preprint arXiv:2501.01329. https://arxiv.org/abs/2501.01329
Bosi Wen, Pei Ke, Yufei Sun, Cunxiang Wang, Xiaotao Gu, Jinfeng Zhou, Jie Tang, Hongning Wang, Minlie Huang, 31 May 2025 (v2), HPSS: Heuristic Prompting Strategy Search for LLM Evaluators https://arxiv.org/abs/2502.13031 https://github.com/thu-coai/HPSS
Santosh Kumar Radha, Yasamin Nouri Jelyani, Ara Ghukasyan, Oktay Goktas, 19 Sep 2024, Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning, https://arxiv.org/abs/2409.12618

Advanced Prompt Engineering Techniques

Research papers on advanced prompting methods:

Heiko Hotz, Sep 2024, Automated Prompt Engineering: The Definitive Hands-On Guide, https://towardsdatascience.com/automated-prompt-engineering-the-definitive-hands-on-guide-1476c8cd3c50
Bijit Ghosh Feb 2024, Advanced Prompt Engineering for Reducing Hallucination, https://medium.com/@bijit211987/advanced-prompt-engineering-for-reducing-hallucination-bb2c8ce62fc6
Louis-François Bouchard, Aug 03, 2024, The Myth of Advanced Prompting: Making Simple Things Sound Complicated, https://louisbouchard.substack.com/p/the-myth-of-advanced-prompting-making
Asankhaya Sharma (codelion), Sep 2024, Optillm: Optimizing inference proxy for LLMs, https://github.com/codelion/optillm
Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, Pranav Sandeep Dulepet, Saurav Vidyadhara, Dayeon Ki, Sweta Agrawal, Chau Pham, Gerson Kroiz, Feileen Li, Hudson Tao, Ashay Srivastava, Hevander Da Costa, Saloni Gupta, Megan L. Rogers, Inna Goncearenco, Giuseppe Sarli, Igor Galynker, Denis Peskoff, Marine Carpuat, Jules White, Shyamal Anadkat, Alexander Hoyle, Philip Resnik, 17 Jun 2024 (v2), The Prompt Report: A Systematic Survey of Prompting Techniques, https://arxiv.org/abs/2406.06608 https://trigaten.github.io/Prompt_Survey_Site/
Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-guang Lou, 29 Feb 2024 (v2), Re-Reading Improves Reasoning in Large Language Models, https://arxiv.org/abs/2309.06275
Latent Space, Sep 20, 2024, The Ultimate Guide to Prompting: Why DSPy is underrated, how to do few-shots properly, why role based prompting doesn't work, and how to HackAPrompt https://www.latent.space/p/learn-prompting
Yuan-Feng Song, Yuan-Qin He, Xue-Fang Zhao, Han-Lin Gu, Di Jiang, Hai-Jun Yang, Li-Xin Fan, July 2024, A communication theory perspective on prompting engineering methods for large lan guage models. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(4): 984−1004 July 2024. DOI: 10.1007/s11390-024-4058-8, https://doi.org/10.1007/s11390-024-4058-8 https://jcst.ict.ac.cn/en/article/pdf/preview/10.1007/s11390-024-4058-8.pdf
Daphne van Zandvoort, Laura Wiersema, Tom Huibers, Sandra van Dulmen, Sjaak Brinkkemper, 19 Jan 2024 (v2), Enhancing Summarization Performance through Transformer-Based Prompt Engineering in Automated Medical Reporting, https://arxiv.org/abs/2311.13274
Chung-Yu Wang, Alireza DaghighFarsoodeh, Hung Viet Pham, 24 Sep 2024, Task-oriented Prompt Enhancement via Script Generation, https://arxiv.org/abs/2409.16418
Tongxuan Liu, Wenjiang Xu, Weizhe Huang, Xingyu Wang, Jiaxing Wang, Hailong Yang, Jing Li, 26 Sep 2024, Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models, https://arxiv.org/abs/2409.17539
Yi Cheng, Xiao Liang, Yeyun Gong, Wen Xiao, Song Wang, Yuji Zhang, Wenjun Hou, Kaishuai Xu, Wenge Liu, Wenjie Li, Jian Jiao, Qi Chen, Peng Cheng, Wayne Xiong, Integrative Decoding: Improve Factuality via Implicit Self-consistency, 3 Oct 2024 (v2), https://arxiv.org/abs/2410.01556 (Prepends a previous response to improve decoding accuracy.)
Jose Antonio Lanz, Oct 15, 2024, OpenAI Unveils Secret Meta Prompt—And It’s Very Different From Anthropic's Approach, Under the hood, OpenAI’s structured approach to prompt generation sets it apart from Anthropic’s human-like, chatbot approach. https://decrypt.co/285854/openai-secret-meta-prompt-anthropic
Vishal Rajput, Oct 2024, The Prompt Report: Prompt Engineering Techniques, https://medium.com/aiguys/the-prompt-report-prompt-engineering-techniques-254464b0b32b
Till Döhmen, 2024/10/17, Introducing the prompt() Function: Use the Power of LLMs with SQL! https://motherduck.com/blog/sql-llm-prompt-function-gpt-models/
Afghan Bitani, Sep 4, 2024, How to Make ChatGPT Write Like a Human: (7-Step Prompt) to Make Your Content Come Alive! I tried a total of 58 different prompts in my experiments, Out of these, 7 truly stand out. https://medium.com/@afghanbitani/how-to-make-chatgpt-write-like-a-human-7-step-prompt-to-make-your-content-come-alive-98e0cd51894f
Mohi Reza, Ioannis Anastasopoulos, Shreya Bhandari, Zachary A. Pardos, 21 Oct 2024], PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation, https://arxiv.org/abs/2410.16547
Foundry AI, Oct 2024, When Should You Move Beyond Prompting and Start Fine-Tuning? https://thefoundryai.com/blog/fine-tuning
Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu, 5 Sep 2024 (v5), Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review, https://arxiv.org/abs/2310.14735
Cobus Greyling, Aug 2, 2023, 12 Prompt Engineering Techniques, https://cobusgreyling.medium.com/12-prompt-engineering-techniques-644481c857aa
Mike Taylor Oct 29, 2024, Five proven prompt engineering techniques (and a few more-advanced tactics), https://www.lennysnewsletter.com/p/five-proven-prompt-engineering-techniques
Mandana Vaziri, Louis Mandel, Claudio Spiess, Martin Hirzel, 24 Oct 2024, PDL: A Declarative Prompt Programming Language, https://arxiv.org/abs/2410.19135
Jordan Gibbs, Oct 2024, Become a Master Prompt Engineer with ChatGPT Canvas. Write perfect prompts in a fraction of the time with my new GPT Canvas workflow. https://medium.com/@jordan_gibbs/become-a-master-prompt-engineer-with-chatgpt-canvas-c4eebb8e4c23
Cameron R. Wolfe, Nov 04, 2024, Automatic Prompt Optimization. Practical techniques for improving prompt quality without manual effort, https://cameronrwolfe.substack.com/p/automatic-prompt-optimization
Manpreet Singh, Oct 31, 2024, Let's Simplifying How We Talk to AI Using Prompt Declaration Language (PDL), https://pub.towardsai.net/lets-simplifying-how-we-talk-to-ai-using-prompt-declaration-language-pdl-b1824c4de833
Salvatore Raieli, Nov 2024, What Is The Best Therapy For a Hallucinating AI Patient? Exploring the Art and Science of Prompt Engineering to Cure LLM Hallucinations, https://levelup.gitconnected.com/what-is-the-best-therapy-for-a-hallucinating-ai-patient-acf0cb9b3e00
Hanqing Li, Diego Klabjan, 11 Nov 2024, Reverse Prompt Engineering, https://arxiv.org/abs/2411.06729
Ethan Mollick, Nov 24, 2024, Getting started with AI: Good enough prompting. Don't make this hard. https://www.oneusefulthing.org/p/getting-started-with-ai-good-enough
Dynomight, Nov 2024, OK, I can partly explain the LLM chess weirdness now, https://dynomight.net/more-chess/
Harsha Nori, Naoto Usuyama, Nicholas King, Scott Mayer McKinney, Xavier Fernandes, Sheng Zhang, Eric Horvitz, 6 Nov 2024, From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond, https://arxiv.org/abs/2411.03590
Ian Drosos, Jack Williams, Advait Sarkar, Nicholas Wilson, 3 Dec 2024, Dynamic Prompt Middleware: Contextual Prompt Refinement Controls for Comprehension Tasks, https://arxiv.org/abs/2412.02357
Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz, Adam Fourney, Hussein Mozannar, Victor Dibia, and Daniel S. Weld, Dec 2024, Challenges in Human-Agent Communication, https://www.microsoft.com/en-us/research/uploads/prod/2024/12/HCAI_Agents.pdf
Krishnasai Addala, Kabir Dev Paul Baghel, Chhavi Kirtani, Avinash Anand, Rajiv Ratn Shah, 6 Dec 2024, Steps are all you need: Rethinking STEM Education with Prompt Engineering, https://arxiv.org/abs/2412.05023
Antony Matthews, Dec 2024, 5 Mega ChatGPT Prompts that I Use Everyday to Save 4+ Hours, https://ai.plainenglish.io/5-mega-chatgpt-prompts-that-i-use-everyday-to-save-4-hours-21efeb1918c5
Waleed Kadous, May 17, 2023, Numbers every LLM Developer should know, https://www.anyscale.com/blog/num-every-llm-developer-should-know (Includes discussion of "be concise" prompting.)
Akshay Nambi , Tanuja Ganu , December 17, 2024, PromptWizard: The future of prompt optimization through feedback-driven self-evolving prompts, https://www.microsoft.com/en-us/research/blog/promptwizard-the-future-of-prompt-optimization-through-feedback-driven-self-evolving-prompts/
Daan Kepel, Konstantina Valogianni, 25 Jun 2024, Autonomous Prompt Engineering in Large Language Models, https://arxiv.org/abs/2407.11000
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena, 30 Nov 2021, Show Your Work: Scratchpads for Intermediate Computation with Language Models, https://arxiv.org/abs/2112.00114
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa, 29 Jan 2023 (v4), Large Language Models are Zero-Shot Reasoners, https://arxiv.org/abs/2205.11916 https://github.com/kojima-takeshi188/zero_shot_cot ("Let's think step by step" prepended to every prompt for a type of zero-shot CoT.)
Xiangjue Dong, Maria Teleki, James Caverlee, 18 Dec 2024, A Survey on LLM Inference-Time Self-Improvement, https://arxiv.org/abs/2412.14352 https://github.com/dongxiangjue/Awesome-LLM-Self-Improvement (Broad survey of reasoning improvement methods from multi-step inference to RALM to decoding algorithms.)
Shiv Sakhuja, 25 Sep 2024, Chain-of-Thought (CoT) Prompting Explained: 7 Techniques for Optimizing AI Performance, https://hub.athina.ai/athina-originals/guides-chain-of-thought-cot-prompting-explained-7-techniques-for-optimizing-ai-performance/
Andrea Matarazzo, Riccardo Torlone, 3 Jan 2025, A Survey on Large Language Models with some Insights on their Capabilities and Limitations, https://arxiv.org/abs/2501.04040 (Broad survey with many LLM topics covered from history to architectures to optimizations.)
Ben Hylak and swyx & Alessio, Jan 12, 2025, o1 isn’t a chat model (and that’s the point): How Ben Hylak turned from ol pro skeptic to fan by overcoming his skill issue. https://www.latent.space/p/o1-skill-issue (Prompting reasoning models like "o1" is different from previous model generations.)
Kyle Wiggers, January 9, 2025, ChatGPT’s newest feature lets users assign it traits like ‘chatty’ and ‘Gen Z’, https://techcrunch.com/2025/01/09/chatgpts-newest-feature-lets-user-assign-it-traits-like-chatty-and-gen-z/
David Gewirtz, Jan. 15, 2025 , The five biggest mistakes people make when prompting an AI, https://www.zdnet.com/article/the-five-biggest-mistakes-people-make-when-prompting-an-ai/
Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
Krish Maniar and William Fu-Hinthorn, LangChain, Jan 28, 2025, Exploring Prompt Optimization, https://blog.langchain.dev/exploring-prompt-optimization/ (Long article evaluating various LLMs for automatic prompt optimization.)
Son, M., Won, Y.-J., & Lee, S. (2025). Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques. Applied Sciences, 15(3), 1430. https://doi.org/10.3390/app15031430 https://www.mdpi.com/2076-3417/15/3/1430
Alberto Romero, Feb 06, 2025, AGI Is Already Here—It’s Just Not Evenly Distributed: Or: why you should learn to prompt AI models, https://open.substack.com/pub/thealgorithmicbridge/p/agi-is-already-hereits-just-not-evenly
Nikta Gohari Sadr, Sangmitra Madhusudan, Ali Emami, 5 Feb 2025, Think or Step-by-Step? UnZIPping the Black Box in Zero-Shot Prompts, https://arxiv.org/abs/2502.03418
Avinash Patil, 5 Feb 2025, Advancing Reasoning in Large Language Models: Promising Methods and Approaches, https://arxiv.org/abs/2502.03671
Fabio Matricardi, Jan 18, 2025, How a Small Language Model Can Achieve 100% Accuracy: In Context Learning is Underrated — ICL is the secret key to reach performance boosting — teach to an AI how to say “I don’t know” — part 2, https://generativeai.pub/how-a-small-language-model-can-achieve-100-accuracy-323a789ffa83
Ling Yang, Zhaochen Yu, Bin Cui, Mengdi Wang, 10 Feb 2025, ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates, https://arxiv.org/abs/2502.06772 https://github.com/Gen-Verse/ReasonFlux (RALM-like retrieval of reasoning prompt templates at inference time.)
Daniel Fleischer, Moshe Berchansky, Gad Markovits, Moshe Wasserblat, 13 Feb 2025, SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models, https://arxiv.org/abs/2502.09390 https://github.com/IntelLabs/RAG-FiT/tree/square
Sharaku Satoh, Sep 15, 2024, How to write system prompt for OpenAI’s Custom GPTs, https://medium.com/@sharakusatoh/how-to-write-system-prompt-for-openais-custom-gpts-c8c6bb6d3e31
X Li, C Jiang, Nov 2024, Optimizing Prompt Engineering Methods for Enhanced Logical Reasoning in Transformer Models, RMEL ’24, November 4–7, 2024, Hangzhou, China, https://www.researchgate.net/profile/Xiaoyan-Li-42/publication/389182048_Optimizing_Prompt_Engineering_Methods_for_Enhanced_Logical_Reasoning_in_Transformer_Models/links/67b82fa9461fb56424e3fc72/Optimizing-Prompt-Engineering-Methods-for-Enhanced-Logical-Reasoning-in-Transformer-Models.pdf https://github.com/xiaoyanLi629/RMELS2024
Thuwarakesh Murallie, March 2025, Prompt Engineering (for Everyday Tasks) Doesn’t Have to Be So Complicated: Here’s your quick checklist to get high-quality responses every time, https://ai.gopubby.com/prompt-engineering-for-everyday-tasks-5360dbea357a
Jim the AI Whisperer, Mar 10, 2025, My “Prompt Grafting” technique outperforms Deep Research in head-to-head test — and is 5900% faster: My prompt gets more insightful, comprehensive research from AI, https://generativeai.pub/prompt-grafting-vs-deep-research-faster-better-ai-essays-824968b1a47a
Yexiang Liu, Zekun Li, Zhi Fang, Nan Xu, Ran He, Tieniu Tan, 16 May 2025, Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory, https://arxiv.org/abs/2505.10981
Algo Insights, Apr 14, 2025, Google’s 69-Page Prompt Engineering Masterclass: What’s Inside, https://medium.com/coding-nexus/googles-69-page-prompt-engineering-masterclass-what-s-inside-099fa1466035
Ziqi Yin, Hao Wang, Kaito Horio, Daisuke Kawahara, Satoshi Sekine, 14 Oct 2024 (v2), Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance, https://arxiv.org/abs/2402.14531
David Gewirtz, June 19, 2025, 8 ways to write better ChatGPT prompts - and get the results you want faster: Making the most of AI takes practice. Optimizing your inputs can improve ChatGPT's outputs. Here's how, https://www.zdnet.com/article/8-ways-to-write-better-chatgpt-prompts-and-get-the-results-you-want-faster/
Kwesi Cobbina, Tianyi Zhou, 30 Jul 2025, Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning, https://arxiv.org/abs/2507.22887 (Best place to put examples is at the start of a prompt not the end.)
Lingrui Mei, Jiayu Yao, Yuyao Ge, Yiwei Wang, Baolong Bi, Yujun Cai, Jiazhi Liu, Mingyu Li, Zhong-Zhi Li, Duzhen Zhang, Chenlin Zhou, Jiayi Mao, Tianze Xia, Jiafeng Guo, Shenghua Liu, 21 Jul 2025 (v2), A Survey of Context Engineering for Large Language Models, https://arxiv.org/abs/2507.13334
Elvis, Jul 06, 2025, Context Engineering Guide: Prompt engineering is being rebranded as context engineering, https://nlp.elvissaravia.com/p/context-engineering-guide
Taryn Plumb, June 27, 2025, The rise of prompt ops: Tackling hidden AI costs from bad inputs and context bloat, https://venturebeat.com/ai/the-rise-of-prompt-ops-tackling-hidden-ai-costs-from-bad-inputs-and-context-bloat/
Fluxus, Aug 2025, Why Your AI Never Works on the First Try, https://fluxus.io/article/why-your-ai-never-works-on-the-first-try
David Cummings, June 28, 2025, Use Advanced Prompts for More AI Value, https://davidcummings.org/2025/06/28/use-advanced-prompts-for-more-ai-value/
Torsten Walbaum, Aug 13, 2025, How to use Deep Research for GTM: You could save 10+ hours per week with this underused AI function, https://www.growthunhinged.com/p/deep-research-for-gtm
Anoop Kotha, Julian Lee, Eric Zakariasson, Anoop Kotha, Julian Lee, OpenAI, Aug 2025, GPT-5 prompting guide, https://cookbook.openai.com/examples/gpt-5/gpt-5_prompting_guide

Prompt Efficiency Optimizations

There are several types of speed optimizations of LLM inference that involve prompt tokens. The main ideas are:

Prompt compression — fewer tokens to process.
Prompt caching — storing and reusing the outputs or KV cache data.
Parallel processing — e.g., skeleton-of-thought prompting.

Prompt compression research. Various prompt compression techniques include:

Prompt caching research. The various types of caching may include:

General Research on Prompt Engineering

David Gewirtz, April 19, 2024, Logitech's free AI Prompt Builder is surprisingly handy, https://www.zdnet.com/article/logitechs-free-ai-prompt-builder-is-surprisingly-handy-heres-how-im-using-it/
Jinliang Yuan, Chen Yang, Dongqi Cai, Shihe Wang, Xin Yuan, Zeling Zhang, Xiang Li, Dingge Zhang, Hanzi Mei, Xianqing Jia, Shangguang Wang, Mengwei Xu, 12 Mar 2024, Mobile Foundation Model as Firmware (v4), https://arxiv.org/abs/2308.14363 (Runs a 10B LLM named "M4" based on Llama and Llama-2 on a Google Pixel 7 Pro, including use of 4-bit, 8-bit and 16-bit quantized versions of the M4 model.)
Ayush Thakur, 2024, The Art of Prompting: Unleashing the Power of Large Language Models, PDF: https://www.researchgate.net/profile/Ayush-Thakur-9/publication/379044941_The_Art_of_Prompting_Unleashing_the_Power_of_Large_Language_Models/links/65f85d0e32321b2cff8c3104/The-Art-of-Prompting-Unleashing-the-Power-of-Large-Language-Models.pdf
Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhijun Tu, Kai Han, Hailin Hu, Dacheng Tao, 5 Feb 2024. A Survey on Transformer Compression. https://arxiv.org/abs/2402.05964 (Model compression survey paper with focus on pruning, quantization, knowledge distillation, and efficient architecture design.)
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, Christopher Potts, 5 Oct 2023, DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines, https://arxiv.org/abs/2310.03714 Code: https://github.com/stanfordnlp/dspy
Bijit Ghosh Feb 2024, Advanced Prompt Engineering for Reducing Hallucination, https://medium.com/@bijit211987/advanced-prompt-engineering-for-reducing-hallucination-bb2c8ce62fc6
Yuechen Zhang, Shengju Qian, Bohao Peng, Shu Liu, Jiaya Jia, 7 Dec 2023, Prompt Highlighter: Interactive Control for Multi-Modal LLMs, https://arxiv.org/abs/2312.04302 Code: https://github.com/dvlab-research/Prompt-Highlighter/ (Allows users to highlight part of their prompt for more specificity.)
Jinyu Chen, Wenchao Xu, Zicong Hong, Song Guo, Haozhao Wang, Jie Zhang, Deze Zeng, 10 Jan 2024, OTAS: An Elastic Transformer Serving System via Token Adaptation, https://arxiv.org/abs/2401.05031
Cobus Greyling, Dec 2023, A Comprehensive Survey of Large Language Models (LLMs), https://cobusgreyling.medium.com/a-comprehensive-survey-of-large-language-models-llms-946a30d9288e
Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, Pranav Sandeep Dulepet, Saurav Vidyadhara, Dayeon Ki, Sweta Agrawal, Chau Pham, Gerson Kroiz, Feileen Li, Hudson Tao, Ashay Srivastava, Hevander Da Costa, Saloni Gupta, Megan L. Rogers, Inna Goncearenco, Giuseppe Sarli, Igor Galynker, Denis Peskoff, Marine Carpuat, Jules White, Shyamal Anadkat, Alexander Hoyle, Philip Resnik, 6 Jun 2024, The Prompt Report: A Systematic Survey of Prompting Techniques, https://arxiv.org/abs/2406.06608
Alexey Evdokimov, May 7, 2024, Do Prompt Structures Improve Output Quality? Testing Zero-Shot Prompts with GPT-4, Claude 3 and Gemini 1.5, https://ai.gopubby.com/do-prompt-structures-improve-output-quality-testing-prompts-with-gpt-4-claude-3-and-gemini-1-5-4b0eaa286625
Julian Yip, May 6, 2024, Prompt Like a Data Scientist: Auto Prompt Optimization and Testing with DSPy: Applying machine learning methodology to prompt building, Towards Data Science, https://towardsdatascience.com/prompt-like-a-data-scientist-auto-prompt-optimization-and-testing-with-dspy-ff699f030cb7
Practicus AI, Jun 11, 2024, The 5 Prompt Engineering Techniques AI Engineers Need to Know, https://levelup.gitconnected.com/the-5-prompt-engineering-techniques-ai-engineers-need-to-know-a208af13d8e4
Bin Xiao, Burak Kantarci, Jiawen Kang, Dusit Niyato, Mohsen Guizani, 18 Jun 2024 (v2), Efficient Prompting for LLM-based Generative Internet of Things, https://arxiv.org/abs/2406.10382
Lak Lakshmanan, March 7, 2024, Building an AI Assistant with DSPy: A way to program and tune prompt-agnostic LLM agent pipelines, https://towardsdatascience.com/building-an-ai-assistant-with-dspy-2e1e749a1a95
Sachit Menon, Richard Zemel, Carl Vondrick, 20 Jun 2024, Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities, https://arxiv.org/abs/2406.14562
Honghua Dong, Qidong Su, Yubo Gao, Zhaoyu Li, Yangjun Ruan, Gennady Pekhimenko, Chris J. Maddison, Xujie Si, 19 Jun 2024, APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts, https://arxiv.org/abs/2406.13161 Code: https://github.com/appl-team/appl (A Python-like script language for prompt engineering integration into applications and agents.)
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, Renqian Luo, Scott Mayer McKinney, Robert Osazuwa Ness, Hoifung Poon, Tao Qin, Naoto Usuyama, Chris White, Eric Horvitz, 28 Nov 2023, Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine, https://arxiv.org/abs/2311.16452
Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie, 12 Nov 2023 (v7), Large Language Models Understand and Can be Enhanced by Emotional Stimuli, https://arxiv.org/abs/2307.11760 https://llm-enhance.github.io/
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen, 15 Apr 2024 (v3), Large Language Models as Optimizers, https://arxiv.org/abs/2309.03409
Teknium, 2024, Prompt Engineering Tool, https://github.com/teknium1/Prompt-Engineering-Toolkit
Mykel Nahorniak, June 12th, 2024, LLM Prompting for Software Development, https://www.mykel.org/notes/llm-prompting-for-software-development Code: https://github.com/mykeln/todo-example
Daniel Lopes, June 21, 2024, A Comprehensive Guide to Text Prompt Engineering Techniques, https://journal.daniellopes.dev/p/practical-prompt-engineering-notes
Eugene Yan, 2024, Prompting Fundamentals and How to Apply them Effectively, https://eugeneyan.com/writing/prompting/
Louis-François Bouchard, Louie Peters, May 2024, Chapter 4: Prompting, and Chapter 6, Prompting with LangChain, Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG, https://www.amazon.com/Building-LLMs-Production-Reliability-Fine-Tuning/dp/B0D4FFPFW8/
Valentina Alto, 2024, Chapter 4: Prompt Engineering, Building LLM-Powered Applications: Create intelligence apps and agents with large language models, Packt Publishing, https://www.amazon.com/Building-LLM-Apps-Intelligent-Language/dp/1835462316/
Aarushi Kansal, Chapter 8: Prompt Engineering Techniques, Building Generative AI-Powered Apps: A Hands-on Guide for Developers, Apress, https://www.amazon.com/Building-Generative-AI-Powered-Apps-Hands-ebook/dp/B0CTXXP1S4/
Google, 2024, Use system instructions to steer the behavior of a model, https://ai.google.dev/gemini-api/docs/system-instructions?lang=python
Yi Zhou, Dec 16, 2023, Optimizing GenAI: Comparing Model Training, Fine-Tuning, RAG, and Prompt Engineering, https://medium.com/generative-ai-revolution-ai-native-transformation/optimizing-genai-comparing-model-training-fine-tuning-rag-and-prompt-engineering-7a7c6c65e0f0
Alexey Evdokimov, Jun 8, 2024, 12 Questions to Consider When Using AI: Path to AI Power User, https://ai.gopubby.com/12-questions-to-consider-when-using-ai-path-to-ai-power-user-9c7e8de1f8b7
Simon Bisson, Jul 25, 2024, Build and manage LLM prompts with Prompty, https://www.infoworld.com/article/3477435/build-and-manage-llm-prompts-with-prompty.html
James Groeneveld, Aug 1, 2024, Prompt Design at Character.AI, Character.AI blog, https://research.character.ai/prompt-design-at-character-ai/
Louis-François Bouchard, Aug 03, 2024, The Myth of Advanced Prompting: Making Simple Things Sound Complicated, https://louisbouchard.substack.com/p/the-myth-of-advanced-prompting-making
Marko Zivkovic, Aug 06, 2024, Discovered Apple Intelligence prompts show Apple's attempt at preventing AI disaster, https://appleinsider.com/articles/24/08/06/discovered-apple-intelligence-prompts-show-apples-attempt-at-preventing-ai-disaster
Gilbert Mizrahi, Jan 2024, Unlocking the Secrets of Prompt Engineering: Master the art of creative language generation to accelerate your journey from novice to pro, Packt Publishing, https://www.amazon.com/Unlocking-Secrets-Prompt-Engineering-generation/dp/1835083838/
Aymen El Amri, Aug 2023, LLM Prompt Engineering For Developers: The Art and Science of Unlocking LLMs' True Potential, Faun, https://www.amazon.com/dp/B0CH43Q839/
Russel Grant, November 28, 2023 Prompt Engineering and ChatGPT: How to Easily 10X Your Productivity, Creativity, and Make More Money Without Working Harder, https://www.amazon.com/dp/1962079066/
Nathan Hunter, January 19, 2023 The Art of Prompt Engineering with chatGPT: A Hands-On Guide (Learn AI Tools the Fun Way!), https://www.amazon.com/Art-Prompt-Engineering-chatGPT-Hands/dp/1739296710/
Yi Zhou, Oct 2023, Prompt Design Patterns: Mastering the Art and Science of Prompt Engineering (Generative AI Revolution Series), https://www.amazon.com/Prompt-Design-Patterns-Engineering-Generative/dp/B0CL2JP16V/
Vajo Lukic, May 2024, Practical Prompt Engineering: A Step-by-Step Guide to Using AI Language Models, https://www.amazon.com/Practical-Prompt-Engineering-Step-Step/dp/B0D367JBHT/
Dr. Lance Eliot, March 20, 2024 Advances In Prompt Engineering For Generative AI: Practical Advances In Artificial Intelligence And Machine Learning, https://www.amazon.com/Advances-Prompt-Engineering-Generative-Intelligence/dp/1957386177/
Ethan James Whitfield, Jan 2024, ChatGPT Prompt Engineering: Advanced Prompts Strategies and Expert Tips for Achieving Financial Success Through AI, https://www.amazon.com/ChatGPT-Prompt-Engineering-Strategies-Achieving-ebook/dp/B0CT73T2M5/
David Scott Bernstein, Aug 2023, Prompt Engineering for Everyone: A Comprehensive Guide to Unlock the Potential of ChatGPT and AI-Language Models, https://www.amazon.com/Prompt-Engineering-Everyone-Comprehensive-AI-Language/dp/B0CFCZCJNG/
John Berryman, Albert Ziegler, January 28, 2025, Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications, ‎ O'Reilly Media, https://www.amazon.com/Prompt-Engineering-LLMs-Model-Based-Applications/dp/1098156153/
Apurv Sibal, February 26, 2025, Hands-On Prompt Engineering: Learning to Program ChatGPT Using OpenAI APIs, Wiley, https://www.amazon.com/Hands-Prompt-Engineering-Learning-Program/dp/1394210760/
James Phoenix, Mike Taylor, June 25, 2024, Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs, O'Reilly Media, https://www.amazon.com/Prompt-Engineering-Generative-AI-Future-Proof/dp/109815343X/
Sivesh Sukumar, Nov 21, 2023, We’ve got the picks and shovels — so what’s stopping the gold miners? https://medium.com/balderton/weve-got-the-picks-and-shovels-so-what-s-stopping-the-gold-miners-f39544f0f321
Google, Aug 2024, Prompt gallery, https://ai.google.dev/gemini-api/prompts
Antonis Iliakis, Jul 5, 2024, Amazing Chat GPT Prompts That Will Take You to The Next Level — Part 3, https://generativeai.pub/i-asked-chat-gpt-to-think-like-a-human-heres-what-i-found-out-7a6017109d66
https://levelup.gitconnected.com/zero-to-hero-crafting-a-custom-gpt-e2ef22653b1f
Emilia David, September 3, 2024, Anthropic to release system prompts for Artifacts, latest Claude family prompts found incomplete, https://venturebeat.com/ai/anthropic-to-release-system-prompts-for-artifacts-latest-claude-family-prompts-found-incomplete/
Emilia David, August 27, 2024, Anthropic releases AI model system prompts, winning praise for transparency, https://venturebeat.com/ai/anthropic-releases-ai-model-system-prompts-winning-praise-for-transparency/
Tiernan Ray, Sept. 4, 2024, Google's Gems are a gentle introduction to AI prompt engineering: Google's pre-built Gems offer prompt examples you can modify to get started with your own custom bot, https://www.zdnet.com/article/googles-gems-are-a-gentle-introduction-to-ai-prompt-engineering/
David Gewirtz, Sept. 5, 2024, 6 ways to write better ChatGPT prompts - and get the results you want faster, https://www.zdnet.com/article/6-ways-to-write-better-chatgpt-prompts-and-get-the-results-you-want-faster/
Kris Ograbek, Aug 30, 2024, 6 Hard-learned Lessons from My First Project as a Freelance AI Engineer, https://ai.gopubby.com/6-hard-learned-lessons-from-my-first-project-as-a-freelance-ai-engineer-9519e6edee90
Heiko Hotz, Sep 2024, Automated Prompt Engineering: The Definitive Hands-On Guide, https://towardsdatascience.com/automated-prompt-engineering-the-definitive-hands-on-guide-1476c8cd3c50
Emilia David, September 10, 2024, ServiceNow introduces a library of enterprise AI agents you can customize to fit your workflow, https://venturebeat.com/ai/servicenow-introduces-a-library-of-enterprise-ai-agents-you-can-customize-to-fit-your-workflow/
Asankhaya Sharma (codelion), Sep 2024, Optillm: Optimizing inference proxy for LLMs, https://github.com/codelion/optillm
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha, 5 Feb 2024, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, https://arxiv.org/abs/2402.07927
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe, 31 May 2023, Let's Verify Step by Step, https://arxiv.org/abs/2305.20050
Emilia David, September 12, 2024, How to prompt on OpenAI’s new o1 models, https://venturebeat.com/ai/how-to-prompt-on-openai-o1/ (Prompt engineering is different for o1, such as "don't use chain of thought.")
Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-guang Lou, 29 Feb 2024 (v2), Re-Reading Improves Reasoning in Large Language Models, https://arxiv.org/abs/2309.06275
Latent Space, Sep 20, 2024, The Ultimate Guide to Prompting: Why DSPy is underrated, how to do few-shots properly, why role based prompting doesn't work, and how to HackAPrompt https://www.latent.space/p/learn-prompting
Daphne van Zandvoort, Laura Wiersema, Tom Huibers, Sandra van Dulmen, Sjaak Brinkkemper, 19 Jan 2024 (v2), Enhancing Summarization Performance through Transformer-Based Prompt Engineering in Automated Medical Reporting, https://arxiv.org/abs/2311.13274
Deven Panchal, September 22, 2024, Why prompt engineering is one of the most valuable skills today, https://venturebeat.com/ai/why-prompt-engineering-is-one-of-the-most-valuable-skills-today/
Matt Asay, Sep 23, 2024, Too much assembly required for AI, https://www.infoworld.com/article/3536292/too-much-assembly-required-for-ai.html
Laria Reynolds, Kyle McDonell, 15 Feb 2021, Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm, https://arxiv.org/abs/2102.07350
Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu, 5 Sep 2024 (v5), Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review, https://arxiv.org/abs/2310.14735
Cobus Greyling, Aug 2, 2023, 12 Prompt Engineering Techniques, https://cobusgreyling.medium.com/12-prompt-engineering-techniques-644481c857aa
Mike Taylor Oct 29, 2024, Five proven prompt engineering techniques (and a few more-advanced tactics), https://www.lennysnewsletter.com/p/five-proven-prompt-engineering-techniques
Ethan Mollick, Nov 24, 2024, Getting started with AI: Good enough prompting. Don't make this hard. https://www.oneusefulthing.org/p/getting-started-with-ai-good-enough
Mingda Zhang, Na Zhao, Jianglong Qing, Qing xu, Kaiwen Pan, Ting luo, 22 Jul 2025, An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis, https://arxiv.org/abs/2507.07893
Zhenchang Xing, Yang Liu, Zhuo Cheng, Qing Huang, Dehai Zhao, Daniel Sun, Chenhua Liu, 9 Aug 2025, When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction, https://arxiv.org/abs/2508.06942
Xin Zhang, Lissette Iturburu, Juan Nicolas Villamizar, Xiaoyu Liu, Manuel Salmeron, Shirley J.Dyke, Julio Ramirez, 26 Jul 2025, Large Language Model Agent for Structural Drawing Generation Using ReAct Prompt Engineering and Retrieval Augmented Generation, https://arxiv.org/abs/2507.19771
Anas Mohamed, Azal Ahmad Khan, Xinran Wang, Ahmad Faraz Khan, Shuwen Ge, Saman Bahzad Khan, Ayaan Ahmad, Ali Anwar, 27 Jul 2025, Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering, https://arxiv.org/abs/2507.20133
Bushra Asseri, Estabrag Abdelaziz, Areej Al-Wabil, 30 Jul 2025, Prompt Engineering Techniques for Mitigating Cultural Bias Against Arabs and Muslims in Large Language Models: A Systematic Review, https://arxiv.org/abs/2506.18199
Hamed Taherkhani, Melika Sepindband, Hung Viet Pham, Song Wang, Hadi Hemmati, 29 Jul 2025, Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm, https://arxiv.org/abs/2408.11198
Arshia Ilaty, Hossein Shirazi, Hajar Homayouni, 11 Aug 2025, SynLLM: A Comparative Analysis of Large Language Models for Medical Tabular Synthetic Data Generation via Prompt Engineering, https://arxiv.org/abs/2508.08529
Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Nathaniel Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, J. Zico Kolter, 16 Aug 2025, Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation, https://arxiv.org/abs/2403.19103
Federico Errica, Giuseppe Siracusano, Davide Sanvito, Roberto Bifulco, 23 Aug 2025, What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering, https://arxiv.org/abs/2406.12334