🧠 Advanced
Zero-Shot
🟒 Introduction
🟒 Emotion Prompting
🟒 Role Prompting
🟒 Re-reading (RE2)
🟒 Rephrase and Respond (RaR)
🟦 SimToM
β—† System 2 Attention (S2A)
Few-Shot
🟒 Introduction
🟒 Self-Ask
🟒 Self Generated In-Context Learning (SG-ICL)
β—† K-Nearest Neighbor (KNN)
β—†β—† Vote-K
β—†β—† Prompt Mining
Thought Generation
🟒 Introduction
🟦 Contrastive Chain-of-Thought
🟦 Automatic Chain of Thought (Auto-CoT)
🟦 Tabular Chain-of-Thought (Tab-CoT)
🟦 Memory-of-Thought (MoT)
🟦 Active Prompting
🟦 Analogical Prompting
🟦 Complexity-Based Prompting
🟦 Step-Back Prompting
🟦 Thread of Thought (ThoT)
Ensembling
🟒 Introduction
🟦 Mixture of Reasoning Experts (MoRE)
🟦 Consistency-based Self-adaptive Prompting (COSP)
🟦 Max Mutual Information (MMI) Method
🟦 DiVeRSe (Diverse Verifier on Reasoning Step)
🟦 Prompt Paraphrasing
🟦 Universal Self-Adaptive Prompting (USP)
🟒 Universal Self-Consistency
🟦 Multi-Chain Reasoning (MCR)
Self-Criticism
🟒 Introduction
🟒 Self-Calibration
🟒 Chain-of-Verification (CoVe)
🟦 Self-Refine
🟦 Cumulative Reasoning
🟦 Reversing Chain-of-Thought (RCoT)
β—† Self-Verification
Decomposition
🟒 Introduction
🟦 Decomposed Prompting
🟦 Plan-and-Solve Prompting
🟦 Program of Thoughts
🟦 Tree of Thoughts
β—† Faithful Chain-of-Thought
β—† Recursion of Thought
β—† Skeleton-of-Thought
🌱 New Techniques
πŸ‘€ For Vision-Language Models (VLMs)
🟒 Prompt Learning
β—† Context Optimization (CoOp)
β—† Conditional Prompt Learning (CoCoOp)
β—† Mixture of Prompt Learning (MoCoOp)
β—† Attention Prompting on Image
πŸ”€ For Multimodal Large Language Models (MLLMs)
β—† Visual Prompting
πŸ”“ Prompt Hacking
🟒 Defensive Measures
🟒 Introduction
🟒 Filtering
🟒 Instruction Defense
🟒 Post-Prompting
🟒 Random Sequence Enclosure
🟒 Sandwich Defense
🟒 XML Tagging
🟒 Separate LLM Evaluation
🟒 Other Approaches
🟒 Offensive Measures
🟒 Introduction
🟒 Obfuscation/Token Smuggling
🟒 Payload Splitting
🟒 Defined Dictionary Attack
🟒 Virtualization
🟒 Indirect Injection
🟒 Recursive Injection
🟒 Code Injection
πŸ”¨ Tooling
Prompt Engineering IDEs
🟒 Introduction
GPT-3 Playground
Dust
Soaked
Everyprompt
Prompt IDE
PromptTools
PromptSource
PromptChainer
Prompts.ai
Snorkel 🚧
Human Loop
Spellbook 🚧
Kolla Prompt 🚧
Lang Chain
OpenPrompt
OpenAI DALLE IDE
Dream Studio
Patience
Promptmetheus
PromptSandbox.io
The Forge AI
AnySolve
Conclusion

The Glossary

Vocabulary Resource

This glossary provides definitions and explanations of key terms used in prompt engineering and generative AI.

Sander Schulhoff

Sander Schulhoff is the Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.

Footnotes

  1. Brown, T. B. (2020). Language models are few-shot learners. arXiv Preprint arXiv:2005.14165. ↩ ↩2 ↩3

  2. Wu, T., Terry, M., & Cai, C. J. (2022). Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 1–22. ↩

  3. Schulhoff, S., Ilie, M., Balepur, N., Kahadze, K., Liu, A., Si, C., Li, Y., Gupta, A., Han, H., Schulhoff, S., & others. (2024). The Prompt Report: A Systematic Survey of Prompting Techniques. arXiv Preprint arXiv:2406.06608. ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  4. Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020). Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv Preprint arXiv:2010.15980. ↩

  5. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners. ↩

  6. Yasunaga, M., Chen, X., Li, Y., Pasupat, P., Leskovec, J., Liang, P., Chi, E. H., & Zhou, D. (2023). Large language models as analogical reasoners. arXiv Preprint arXiv:2310.01714. ↩

  7. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., & others. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. ↩

  8. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain of Thought Prompting Elicits Reasoning in Large Language Models. ↩

  9. Yew Ken Chia. (2023). Contrastive Chain-of-Thought Prompting. In arXiv preprint arXiv:1907.11692. ↩ ↩2

  10. Tushar Khot. (2023). Decomposed Prompting: A Modular Approach for Solving Complex Tasks. ↩

  11. Li, C., Wang, J., Zhang, Y., Zhu, K., Hou, W., Lian, J., Luo, F., Yang, Q., & Xie, X. (2023). Large language models understand and can be enhanced by emotional stimuli. arXiv Preprint arXiv:2307.11760. ↩

  12. Fu, Y., Peng, H., Sabharwal, A., Clark, P., & Khot, T. (2022). Complexity-based prompting for multi-step reasoning. The Eleventh International Conference on Learning Representations. ↩

  13. Zhou, D., SchΓ€rli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., & Chi, E. (2022). Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. ↩

  14. Lei Wang. (2023). Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models. ↩

  15. Zheng, M., Pei, J., & Jurgens, D. (2023). Is β€œA Helpful Assistant” the Best Role for Large Language Models? A Systematic Evaluation of Social Roles in System Prompts. https://arxiv.org/abs/2311.10054 ↩

  16. Zheng, H. S., Mishra, S., Chen, X., Cheng, H.-T., Chi, E. H., Le, Q. V., & Zhou, D. (2023). Take a step back: Evoking reasoning via abstraction in large language models. arXiv Preprint arXiv:2310.06117. ↩

  17. Lu, A., Zhang, H., Zhang, Y., Wang, X., & Yang, D. (2023). Bounding the capabilities of large language models in open text generation with prompt constraints. arXiv Preprint arXiv:2302.09185. ↩

  18. Zhou, Y., Geng, X., Shen, T., Tao, C., Long, G., Lou, J.-G., & Shen, J. (2023). Thread of thought unraveling chaotic contexts. arXiv Preprint arXiv:2311.08734. ↩

  19. Liu, J., Liu, A., Lu, X., Welleck, S., West, P., Bras, R. L., Choi, Y., & Hajishirzi, H. (2021). Generated Knowledge Prompting for Commonsense Reasoning. ↩

  20. Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 594–611. ↩

  21. Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys (Csur), 53(3), 1–34. ↩

  22. Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2023). Pal: Program-aided language models. International Conference on Machine Learning, 10764–10799. ↩

  23. Schmidt, D. C., Spencer-Smith, J., Fu, Q., & White, J. (2023). Cataloging prompt patterns to enhance the discipline of prompt engineering. URL: Https://Www. Dre. Vanderbilt. Edu/UndefinedΜƒ Schmidt/PDF/ADA_Europe_Position_Paper. Pdf [Accessed 2023-09-25]. ↩

  24. Wang, Z., Mao, S., Wu, W., Ge, T., Wei, F., & Ji, H. (2024). Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration. https://arxiv.org/abs/2307.05300 ↩

  25. Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022). Self-Consistency Improves Chain of Thought Reasoning in Language Models. ↩

  26. Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2022). What Makes Good In-Context Examples for GPT-3? Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures. https://doi.org/10.18653/v1/2022.deelio-1.10 ↩

  27. Schick, T., & SchΓΌtze, H. (2020). Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference. ↩