Course focus: DAIR.AI's Prompt Engineering Guide collects explanations, papers, techniques, notebooks, and references about prompting, context engineering, RAG, and agents. The guide supports multiple languages.
What you will learn: Beginners can build vocabulary, compare techniques, follow research links, and create a personal checklist for instructions, examples, constraints, output formats, and evaluation.
Recommended order: Begin with introduction and basic prompting sections. Test each technique on the same small task and score the results. Move to retrieval, agents, and advanced reasoning only after a repeatable baseline exists.
Before you begin: Most reading is accessible without local setup. Running notebooks or hosting the guide locally requires the relevant model access and development environment; the repository currently documents Node 18+ for local site development.
Who it suits: Useful for beginners, product teams, and developers who want a broad reference instead of a list of unexplained prompt templates.
Important limits: Prompt behavior varies by model and version. Repository links may also lead to separate paid courses; the GitHub guide itself should be evaluated independently from those offers. Do not paste confidential data into public model services while testing. Build evaluation cases with synthetic or approved data and record the exact model version.
Project and license: Repository content is provided under MIT; check the current LICENSE before reuse. Repository: https://github.com/dair-ai/Prompt-Engineering-Guide
Break the goal into input, action, and output before adding review checkpoints.
Keep examples, failure conditions, and review criteria so the tutorial stays reusable.