GitHub – dair-ai/Prompt-Engineering-Guide: 🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Prompt Engineering Guide — Practical Resource for LLM Development
This post summarizes a comprehensive new resource for prompt engineering and related techniques.
It matters because prompt skills speed up development and reduce risk when you deploy large language models.
Why should you care?
• Learn the latest papers, tools, and lectures.
• Apply reproducible prompt patterns in production.
• Use hands-on courses to close skill gaps.

Header 1 — What is Prompt Engineering
Prompt engineering is the practice of designing and refining inputs to large language models to get reliable outputs.
It helps you understand both the strengths and the limits of LLMs.
Why does that matter to product teams and researchers?
Because small prompt changes often produce large differences in outcomes.
Example prompt snippet.
Prompt: „You are an expert analyst.
Summarize the following customer support conversation in three bullets.“
The snippet shows how to constrain format and role.

Header 2 — What’s Inside the Guide
The guide collects recent papers, learning material, lecture recordings, and tooling links.
It also links to practical courses on prompting, RAG, and AI Agents that follow a hands-on format.
You can run the repository locally to validate implementations or translations.
Example command to get started.
git clone https://github.com/dair-ai/Prompt-Engineering-Guide
Use that repo to inspect prompt templates and reference implementations.

Header 3 — Applying This at Work
Prompt engineering is useful across question answering, arithmetic reasoning, and retrieval-augmented flows.
It helps engineers design robust prompts and integration patterns with external tools.
Need a concrete example for RAG?
Prompt: „Use the retrieved document below to answer the user query.
Cite the paragraph number when you use a fact.“
That pattern reduces hallucinations and simplifies verification.

I recently used the guide to validate a translation prompt for a client.
I found an edge case in how context was truncated.
Fixing the prompt removed inconsistent outputs.

Wrapping up
The guide is a practical, up-to-date resource for teams building with LLMs.
It accelerates learning and encourages reproducible prompt practices.
The impact on GenAI adoption is straightforward.
Better prompts mean more reliable automation and clearer business value.
Explore the repository for direct examples and templates.
https://github.com/dair-ai/Prompt-Engineering-Guide