A deep-dive into RAG system design for architects and tech leads - from naive pipelines to advanced retrieval patterns and agentic RAG, with concrete benchmarks and production trade-offs.

BigData Boutique blog

A comprehensive breakdown of RAG system design covering the full spectrum from naive pipelines to production-grade architectures. Explains why naive RAG (embed-retrieve-generate) fails in production and details advanced patterns including hybrid BM25+dense retrieval (15-30% recall improvement), cross-encoder re-ranking, HyDE query transformation, query decomposition, and contextual chunking strategies. Covers agentic RAG where LLMs autonomously control multi-step, multi-source, self-correcting retrieval. Includes production evaluation guidance (retrieval vs. generation metrics separately using RAGAS/DeepEval), and a decision framework for when to use fine-tuning, long-context prompting, or knowledge graphs instead of RAG. Concrete benchmarks and trade-off tables throughout.

RAG Architecture Explained: How Retrieval-Augmented Generation Actually Works

The Naive RAG Pipeline - and Where It Breaks

Agentic RAG: When Retrieval Becomes a Tool

RAG in Production: Evaluation, Observability, and Knowing When Not to Use RAG