#Agentic RAG

Articles about Agentic RAG — exploring patterns, best practices, and real-world implementations in production systems.

8 posts tagged with agentic rag. ← All posts

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Pratik Dhanave · ·5 min read

Building an Agentic RAG System

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.

#Agentic RAG#RAG#AI Agents#AI Engineering#Evaluation
Pratik Dhanave · ·5 min read

Evaluating Agentic RAG

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?

#Agentic RAG#RAG#Evaluation#AI Engineering#AI Agents
Pratik Dhanave · ·5 min read

Multi-Hop and Iterative Retrieval

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.

#Agentic RAG#RAG#AI Agents#AI Engineering
Pratik Dhanave · ·5 min read

Self-Correcting Retrieval

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.

#Agentic RAG#RAG#AI Engineering#Evaluation#AI Agents
Pratik Dhanave · ·5 min read

Routing and Retrieval as a Tool

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.

#Agentic RAG#RAG#AI Agents#Tool Use#AI Engineering
Pratik Dhanave · ·5 min read

Query Transformation

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.

#Agentic RAG#RAG#AI Engineering#LLMs#AI Agents
Pratik Dhanave · ·5 min read

What Is Agentic RAG?

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.

#Agentic RAG#RAG#AI Agents#AI Engineering
Pratik Dhanave · ·5 min read

Why Naive RAG Falls Short

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#RAG#AI Engineering#LLMs#AI Agents

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.