Retrieval-augmented generation
Chunking, vector search, reranking, citations and evaluation for systems that answer from your own documents.
Part of Artificial intelligence. 4 articles.
How to chunk documents for RAG
Chunking decides what your RAG system can retrieve. Compare fixed-size, recursive, heading-aware and semantic chunking, and learn how to pick size and overlap.
Intermediate3 minHow to evaluate a RAG system: retrieval and answer quality
Measure a RAG system in two layers: did retrieval find the right passages, and is the answer faithful to them? Learn recall@k, MRR, faithfulness and a practical test set.
Intermediate3 minHow vector search works: similarity, top-k and hybrid search
Vector search finds text by meaning instead of exact words. Learn cosine similarity, top-k retrieval, approximate nearest neighbour indexes, BM25 and hybrid ranking.
Intermediate3 minWhat is retrieval-augmented generation (RAG)?
RAG makes a language model answer from your own documents by retrieving relevant passages first. Learn the pipeline step by step, and when RAG is the right choice.
Beginner3 min