Retrieval-Augmented Generation (RAG) is a technique that grounds a language model in external documents. The system retrieves relevant snippets and includes them in the prompt, improving accuracy and reducing hallucinations.

RAG Guides

Learn how to build retrieval-augmented generation systems that are accurate, fast, and production-ready.

RAG from Scratch

A step-by-step guide to building your first retrieval-augmented generation system.

Chunking Strategies

Choose the right chunk size, overlap, and semantic boundaries for your documents.

Evaluation Metrics

Measure retrieval and generation quality with the metrics that matter.

Hybrid Search

Blend keyword and vector retrieval for better recall and precision.