This guide contains a set of papers, learning guides, and tools related to prompt engineering. It includes several materials, guides, examples, papers, and much more. The repo is intended to be used as a research and educational reference for practitioners and developers.
- [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) (Jan 2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) (Nov 2022)
- [Ask Me Anything: A simple strategy for prompting language models](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for) (Oct 2022)
- [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/abs/2205.11916) (May 2022)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) (April 2021)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980) (Oct 2020)
- [BERTese: Learning to Speak to BERT](https://aclanthology.org/2021.eacl-main.316/) (April 2021)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) (Oct 2022)
- [Successive Prompting for Decomposing Complex Questions](https://arxiv.org/abs/2212.04092) (Dec 2022)
- [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) (May 2020)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://arxiv.org/abs/2112.00114) (Nov 2021)
- [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) (Jan 2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) (Feb 2021)
- [Reframing Instructional Prompts to GPTk's Language](https://arxiv.org/abs/2109.07830) (Sep 2021)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755) (Sep 2022)
- [Teaching Algorithmic Reasoning via In-context Learning](https://arxiv.org/abs/2211.09066) (Nov 2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) (Jan 2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295/) (Aug 2021)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/abs/2102.07350) (Feb 2021)
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Apr 2022)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566) (Mar 2022)
- [How Can We Know What Language Models Know?](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00324/96460/How-Can-We-Know-What-Language-Models-Know) (July 2020)
- [Best practices for prompt engineering with OpenAI API](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)
- [A Complete Introduction to Prompt Engineering for Large Language Models](https://www.mihaileric.com/posts/a-complete-introduction-to-prompt-engineering/)
- [3 Principles for prompt engineering with GPT-3](https://www.linkedin.com/pulse/3-principles-prompt-engineering-gpt-3-ben-whately/)
- [Extrapolating to Unnatural Language Processing with GPT-3's In-context Learning: The Good, the Bad, and the Mysterious](http://ai.stanford.edu/blog/in-context-learning/)
