Prompt Engineering Guide — open source GitHub project

Prompt Engineering Guide is an open learning resource for prompting, RAG, agents, and practical work with large language models.

What it is

Prompt Engineering Guide is an open learning project about working with large language models. It grew around a practical realization: answer quality depends not only on the model, but also on task framing, context, examples, output format, and validation.

The dair-ai/Prompt-Engineering-Guide repository has been on GitHub since 2022. Its main format is MDX, it uses the MIT license, and its official site is promptingguide.ai. Topics include prompt engineering, RAG, agents, ChatGPT, OpenAI, and language models, so it is more than a small tip list.

What is inside

Inside are guides, articles, lecture material, notebooks, and research links. The project covers prompt basics, more advanced techniques, retrieval-augmented generation, agent scenarios, and model limitations.

A structured prompt example

This fragment shows why the project is useful: a good prompt defines the task, input context, constraints, and output format. It is not a magic formula; it makes the task easier to evaluate.

Language: Markdown

## Task
Compress text for a catalog card.

## Context
Project: a library for document search.

## Constraints
- 1 sentence
- no promotional tone
- keep the technical meaning

## Format
Return only the final text.

Where it helps

The guide helps developers, analysts, editors, researchers, and product teams that already use LLMs and want more control over output quality. It moves work from random requests toward repeatable patterns.

The limitation sections matter as much as the examples. A prompt does not replace facts, tests, or human review, but it can make the model guess less, follow structure better, and return something easier to evaluate.

Strengths and tradeoffs

The strength is breadth and relevance. It gathers approaches from basic examples to RAG and agent systems, giving teams a shared language for LLM work.

The tradeoff is pace. The field changes faster than any learning resource. A technique that works well on one model may transfer poorly to another, so the material should be treated as a map and a set of testable practices, not eternal rules.