Retrieval-Augmented Generation grounds an agent’s answers in real data instead of relying on what the model memorized. But the naive RAG pipeline — embed, retrieve top-k, stuff into the prompt — breaks down the moment questions get multi-hop, sources get heterogeneous, or freshness matters. Production systems need retrieval that adapts.
These posts cover the engineering reality of RAG in 2026: agentic retrieval where the model decides when and how to fetch context, query routing across semantic, keyword, API, and graph backends, retrieve-then-verify loops that grade their own results, and the guardrails that stop an agent from looping forever. We treat retrieval as a control problem, not a fixed pipeline.
Expect concrete patterns, code sketches, failure-mode analysis, and evaluation strategies — faithfulness, relevance, and answer correctness — drawn from systems we’ve built and operated. Every technique is judged against production constraints: latency budgets, token cost, and the cost of returning a confidently wrong answer.