The Failure of "Naive" RAG
Basic Retrieval-Augmented Generation (RAG) often fails because it treats retrieval as a single, static step. If the vector search returns irrelevant chunks, the model generates a hallucination.
Agentic RAG: Beyond Simple Vector Search
Why naive RAG is failing and how agent-based retrieval patterns are solving the accuracy problem.
Frequently Asked Questions
Why does standard RAG hallucinate?
Because it lacks a feedback loop to verify if the retrieved chunks actually answer the user query.
What is an Agentic Loop in RAG?
A process where the model critiques its retrieval, refines its search query, and iterates until high confidence is met.
Does this increase cost?
Yes, it requires multiple LLM calls per query, but dramatically improves accuracy.
What tools are used for Agentic RAG?
Frameworks like LangGraph and LlamaIndex are currently the industry standards.
Can it handle multi-step reasoning?
Yes, agents can decompose complex questions into sub-questions and aggregate results.