RAG

A systematic guide to lexical retrieval, dense retrieval, hybrid fusion, reranking, evaluation, observability, and production governance in RAG systems.

Explains how to build an observability and evaluation system for RAG that covers runtime execution, retrieval quality, generation quality, and continuous regression testing.

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

Explains the role, calculation, appropriate use cases, and main limitations of RRF in multi-retriever fusion for RAG.

An introduction to the roles of dense search, sparse search, RRF, and rerankers in an enterprise RAG retrieval pipeline.

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

Starting from the trend of platforms absorbing basic RAG pipelines, this article examines the high-premium skills AI application engineers should prioritize in 2026: evaluation and observability, data governance and access control, and deep engineering capabilities for agentic workflows.

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.