Making retrieval reason — why naive RAG falls short, query transformation, routing and retrieval as a tool, self-correcting retrieval, multi-hop iteration, evaluation, and building an agentic RAG system that escalates cost only when a question needs it.
The retrieve-then-generate pipeline that launched a thousand demos hits a wall on real questions, and understanding exactly where it breaks is the case for making retrieval agentic.
The retrieve-then-generate pipeline that launched a thousand demos hits a wall on real questions. Understanding exactly where naive RAG breaks is the case for making retrieval agentic.
Agentic RAG is what you get when retrieval stops being a fixed pipeline step and becomes a set of decisions an agent reasons through — whether to retrieve, what to search for, from where, how many times, and whether to trust the result.
Agentic RAG is what you get when retrieval stops being a fixed pipeline step and becomes a set of decisions an agent reasons through — whether, what, from where, how many times, and whether to trust results.
The user's question is written to be asked, not to be searched, so the first thing an agentic RAG system should do is turn that question into queries that actually retrieve well.
The user's question is written to be asked, not searched, so the first thing an agentic RAG system should do is turn that question into queries that actually retrieve well.
Real systems have more than one place to look, and the answer to "not everything should be retrieved from the same index — or retrieved at all" is to route queries and to treat retrieval as a tool the agent chooses to call.
Real systems have more than one place to look. The answer is to route queries to the right source — and to treat retrieval as a tool the agent chooses to call, or skips entirely.
Naive RAG trusts whatever it retrieved, which is how it produces confident answers grounded in the wrong documents; self-correcting retrieval adds the step it was missing — checking the results before using them.
Naive RAG trusts whatever it retrieved, which is how it produces confident answers grounded in the wrong documents. Self-correcting retrieval adds the missing step: check the results before using them.
Some questions cannot be answered by any single search because the answer is assembled from facts that must be found in sequence, each retrieval informed by the last — and that is what iterative, multi-hop retrieval provides.
Some questions cannot be answered by any single search because the answer is assembled from facts found in sequence, each retrieval informed by the last — that is iterative, multi-hop retrieval.
Every agentic technique in this series adds cost and complexity, so the only way to know any of it is worth it is to measure — and RAG needs measurement on two fronts at once: did it retrieve the right things, and did it answer faithfully from them?
Every agentic technique adds cost, so the only way to know it is worth it is to measure — and RAG needs measurement on two fronts: did it retrieve the right things, and did it answer faithfully from them?
The pieces from this series — routing, query transformation, graded retrieval, multi-hop, and evaluation — assemble into one system that reasons about retrieval as carefully as it reasons about the answer, while spending only as much as each question needs.
Routing, query transformation, graded retrieval, multi-hop, and evaluation assemble into one system that reasons about retrieval as carefully as the answer — while spending only as much as each question needs.
This series is part of a larger body of work by Pratik Dhanave, an Agentic AI Architect writing about production AI systems, distributed systems, and cloud-native engineering. Explore all course series, browse every post, or find topics via the tag index.