Prompt Engineering Guide

Prompt Engineering Guide

The Prompt Engineering Guide by DAIR.AI is the most comprehensive open-source reference for LLM prompting — covering zero-shot, few-shot, chain-of-thought, ReAct, tree-of-thought, self-consistency, generated knowledge, and more. It includes model-specific guidance, prompt templates, research paper summaries, and a growing library of community-contributed examples.

Key features

• Complete technique coverage: zero-shot, few-shot, CoT, ReAct, PAL, tree-of-thought, RAG prompting
• Model-specific notes for GPT-4, Claude, Gemini, Mistral, Llama, and others
• Prompt templates for code generation, summarization, classification, extraction
• Research-backed: cites and explains 100+ prompting papers
• Available as a website, PDF, and raw Markdown
• MIT license — copy, adapt, integrate commercially
• Available in 13 languages

Key prompt patterns

# Chain-of-Thought (CoT) — few-shot example
Q: Roger has 5 tennis balls. He buys 2 more cans, each with 3 balls. How many?
A: Roger started with 5 balls. 2 cans × 3 balls = 6 more. 5 + 6 = 11. The answer is 11.

Q: The cafeteria had 23 apples. They used 20 for lunch and bought 6 more. How many?
A: [Let the model reason step-by-step here]

# ReAct pattern
Thought: I need to find the current population of Tokyo.
Action: Search[Tokyo population 2024]
Observation: Tokyo's population is approximately 13.96 million.
Thought: I have the answer.
Answer: Tokyo's population is approximately 13.96 million.

Install via ai-supply

npx ai-supply add prompt-engineering-guide

Curated mirror of the open-source Prompt Engineering Guide (MIT). Get it from the source.