RAG Retrieval Engineering: From Hybrid Retrieval to Production Governance
A systematic guide to lexical retrieval, dense retrieval, hybrid fusion, reranking, evaluation, observability, and production governance in RAG systems.
HybridRetrieval
The Role of RRF in RAG: A Simple but Not Universal Retrieval-Fusion Method
Explains the role, calculation, appropriate use cases, and main limitations of RRF in multi-retriever fusion for RAG.
Dense + Sparse Hybrid Search in RAG
An introduction to the roles of dense search, sparse search, RRF, and rerankers in an enterprise RAG retrieval pipeline.
A Guide to Building Enterprise RAG Systems
Use this article as a reference when building an enterprise RAG system.
What AI Engineers Should Learn in 2026
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.
The Complete RAG Systems Guide: From Zero to Production
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.
Notes on Anthropic's Contextual Retrieval
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.