Reranking

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.

A RAG reranking design guide for AI Application Engineers. It distinguishes retrieval fusion, semantic reranking, business ranking, and evidence-set selection, then provides a production-ready default pipeline.

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

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.

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.

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.