Prompt Engineering Guide

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. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs). Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.

Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, learning guides, lectures, references, and tools related to prompt engineering.

Happy Prompting!

Announcements / Updates

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🎉 The Prompt Engineering Lecture went live here
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Lecture
Guides
Papers
Tools & Libraries
Datasets
Blog, Guides, Tutorials and Other Readings

Lecture

We have published a 1 hour lecture that provides a comprehensive overview of prompting techniques, applications, and tools.

Guides

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

Papers

The following are the latest papers (sorted by release date) on prompt engineering. We update this on a daily basis and new papers come in. We incorporate summaries of these papers to the guides above every week.

Surveys / Overviews:
- Augmented Language Models: a Survey (Feb 2023)
- A Survey for In-context Learning (Dec 2022)
- Towards Reasoning in Large Language Models: A Survey (Dec 2022)
- Reasoning with Language Model Prompting: 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:
- How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks (March 2023)
- EvoPrompting: Language Models for Code-Level Neural Architecture Search (Feb 2023)
- In-Context Instruction Learning (Feb 2023)
- Chain of Hindsight Aligns Language Models with Feedback (Feb 2023)
- Language Is Not All You Need: Aligning Perception with Language Models (Feb 2023)
- Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data (Feb 2023)
- Active Prompting with Chain-of-Thought for Large Language Models (Feb 2023)
- More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models (Feb 2023)
- A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT (Feb 2023)
- Guiding Large Language Models via Directional Stimulus Prompting (Feb 2023)
- How Does In-Context Learning Help Prompt Tuning? (Feb 2023)
- Scalable Prompt Generation for Semi-supervised Learning with Language Models (Feb 2023)
- Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints (Feb 2023)
- À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting (Feb 2023)
- GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks (Feb 2023)
- The Capacity for Moral Self-Correction in Large Language Models (Feb 2023)
- SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains (Feb 2023)
- Evaluating the Robustness of Discrete Prompts (Feb 2023)
- Compositional Exemplars for In-context Learning (Feb 2023)
- Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery (Feb 2023)
- 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)
- Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (Dec 2022)
- 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)
- Recitation-Augmented 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)
- Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning (Sep 2022)
- Promptagator: Few-shot Dense Retrieval From 8 Examples (Sep 2022)
- DocPrompting: Generating Code by Retrieving the Docs (July 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)
- PPT: Pre-trained Prompt Tuning for Few-shot Learning (Mqy 2022)
- Toxicity Detection with Generative Prompt-based Inference (May 2022)
- Learning to Transfer Prompts for Text Generation (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)
- Training language models to follow instructions with human feedback
- 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)
- Multitask Prompted Training Enables Zero-Shot Task Generalization (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:
- Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis (March 2023)
- SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks (March 2023)
- TabGenie: A Toolkit for Table-to-Text Generation (Feb 2023)
- SGL-PT: A Strong Graph Learner with Graph Prompt Tuning (Feb 2023)
- Few-Shot Table-to-Text Generation with Prompt-based Adapter (Feb 2023)
- Language Models Are Few-shot Learners for Prognostic Prediction (Feb 2023)
- STA: Self-controlled Text Augmentation for Improving Text Classifications (Feb 2023)
- Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback (Feb 2023)
- How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study (Feb 2023)
- Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales (Feb 2023)
- LabelPrompt: Effective Prompt-based Learning for Relation Classification (Feb 2023)
- Language Model Crossover: Variation through Few-Shot Prompting (Feb 2023)
- Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition (Feb 2023)
- The Capacity for Moral Self-Correction in Large Language Models (Feb 2023)
- Prompting for Multimodal Hateful Meme Classification (Feb 2023)
- PLACES: Prompting Language Models for Social Conversation Synthesis (Feb 2023)
- Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation (Feb 2023)
- Crawling the Internal Knowledge-Base of Language Models (Jan 2023)
- Legal Prompt Engineering for Multilingual Legal Judgement Prediction (Dec 2022)
- Investigating Prompt Engineering in Diffusion Models (Nov 2022)
- Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering (Sep 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)

If you are using the guide for your work, please cite us as follows:

@article{Saravia_Prompt_Engineering_Guide_2022,
author = {Saravia, Elvis},
journal = {https://github.com/dair-ai/Prompt-Engineering-Guide},
month = {12},
title = {{Prompt Engineering Guide}},
year = {2022}
}

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

hanjinye/Prompt-Engineering-Guide

Prompt Engineering Guide

Announcements / Updates

Table of Contents

Lecture

Guides

Papers

Tools & Libraries

(Sorted by Name)

Datasets

(Sorted by Name)

Blog, Guides, Tutorials and Other Readings

(Sorted by Name)