Chunking

A concise RAG evaluation reference organized by retrieval, context, answer, citation, refusal, security, and production metrics, with practical formulas and a minimal release-gate set.

An introduction to RAG chunking design, progressing from fixed chunks to structural, contextualized, and evaluation-driven approaches.

Use this article as a reference when building an enterprise RAG system.

For developers learning AI application engineering, this article maps the complete RAG pipeline from ingestion, chunking, retrieval, reranking, and generation to evaluation and operations, and presents an evolution path from a minimal viable solution to production.

Drawing on benchmarks and industry practice from multiple organizations, this guide presents default RAG chunking settings, parameter-tuning methods, and strategies for different document types.

Starting from Anthropic’s Contextual Retrieval article and Appendix II, these notes summarize the core method, experimental findings, and RAG architecture principles suitable for production.