Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics.

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README.md

Prompt Engineering Guide

This guide contains a set of recent papers, learning guides, and tools related to prompt engineering. The repo is intended as a research and educational reference for practitioners and developers.

Announcements:

📣 Full lecture + notebook + exercises on the ~15th of Feb (announcement will happen on Twitter)
📣 Examples and explainers coming soon!
Join our Discord to discuss more about prompt engineering

Guides
Papers
Tools & Libraries
Datasets
Blog, Guides, Tutorials and Other Readings

Guides

The following are a set of guides on prompt engineering developed by us. Guides are work in progress.

Papers

(Sorted by Release Date)

Surveys / Overviews:
- A Survey for In-context Learning (Dec 2022)
- Towards Reasoning in Large Language Models: A Survey (Dec 2022)
- Emergent Abilities of Large Language Models (Jun 2022)
- A Taxonomy of Prompt Modifiers for Text-To-Image Generation (Apr 2022)
- Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (Jul 2021)
Approaches/Techniques:
- Multimodal Chain-of-Thought Reasoning in Language Models (Feb 2023)
- Large Language Models Can Be Easily Distracted by Irrelevant Context (Feb 2023)
- Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models (Feb 2023)
- Progressive Prompts: Continual Learning for Language Models (Jan 2023)
- Batch Prompting: Efficient Inference with LLM APIs (Jan 2023)
- On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning (Dec 2022)
- Constitutional AI: Harmlessness from AI Feedback (Dec 2022)
- Successive Prompting for Decomposing Complex Questions (Dec 2022)
- Discovering Language Model Behaviors with Model-Written Evaluations (Dec 2022)
- Structured Prompting: Scaling In-Context Learning to 1,000 Examples (Dec 2022)
- PAL: Program-aided Language Models (Nov 2022)
- Large Language Models Are Human-Level Prompt Engineers (Nov 2022)
- Ignore Previous Prompt: Attack Techniques For Language Models (Nov 2022)
- Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods (Nov 2022)
- Teaching Algorithmic Reasoning via In-context Learning (Nov 2022)
- Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference (Nov 2022)
- Ask Me Anything: A simple strategy for prompting language models (Oct 2022)
- ReAct: Synergizing Reasoning and Acting in Language Models (Oct 2022)
- Prompting GPT-3 To Be Reliable (Oct 2022)
- Decomposed Prompting: A Modular Approach for Solving Complex Tasks (Oct 2022)
- Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought (Oct 2022)
- Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples (Sep 2022)
- Promptagator: Few-shot Dense Retrieval From 8 Examples (Sep 2022)
- On the Advance of Making Language Models Better Reasoners (June 2022)
- Large Language Models are Zero-Shot Reasoners (May 2022)
- MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning (May 2022)
- Toxicity Detection with Generative Prompt-based Inference (May 2022)
- The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning (May 2022)
- A Taxonomy of Prompt Modifiers for Text-To-Image Generation (Apr 2022)
- PromptChainer: Chaining Large Language Model Prompts through Visual Programming (Mar 2022)
- Self-Consistency Improves Chain of Thought Reasoning in Language Models (March 2022)
- Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (Feb 2022)
- Chain of Thought Prompting Elicits Reasoning in Large Language Models (Jan 2022)
- Show Your Work: Scratchpads for Intermediate Computation with Language Models (Nov 2021)
- Generated Knowledge Prompting for Commonsense Reasoning (Oct 2021)
- Reframing Instructional Prompts to GPTk’s Language (Sep 2021)
- Design Guidelines for Prompt Engineering Text-to-Image Generative Models (Sep 2021)
- Making Pre-trained Language Models Better Few-shot Learners (Aug 2021)
- Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (April 2021)
- BERTese: Learning to Speak to BERT (April 2021)
- The Power of Scale for Parameter-Efficient Prompt Tuning (April 2021)
- Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm (Feb 2021)
- Calibrate Before Use: Improving Few-Shot Performance of Language Models (Feb 2021)
- Prefix-Tuning: Optimizing Continuous Prompts for Generation (Jan 2021)
- AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (Oct 2020)
- Language Models are Few-Shot Learners (May 2020)
- How Can We Know What Language Models Know? (July 2020)
Applications:
- Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation (Feb 2023)
- Legal Prompt Engineering for Multilingual Legal Judgement Prediction (Dec 2022)
- Investigating Prompt Engineering in Diffusion Models (Nov 2022)
- Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language (Oct 2022)
- Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic? (Oct 2022)
- Plot Writing From Scratch Pre-Trained Language Models (July 2022)
Collections:

Tools & Libraries

(Sorted by Name)

Datasets

(Sorted by Name)

Blog, Guides, Tutorials and Other Readings

(Sorted by Name)

Lecture + Tutorial

Full tutorial and lecture coming soon!

Feel free to open a PR if you think something is missing here. Always welcome feedback and suggestions.

Join our Discord

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Prompt Engineering Guide

Table of Contents

Guides

Papers

(Sorted by Release Date)

Tools & Libraries

(Sorted by Name)

Datasets

(Sorted by Name)

Blog, Guides, Tutorials and Other Readings

(Sorted by Name)

Lecture + Tutorial

README.md