Building Agentic RAG Applications

Standard RAG systems are brittle; they retrieve data based on a single query and generate an answer even if the retrieval is poor. Agentic RAG changes the game by giving the LLM “tools” and the autonomy to plan, loop, self-correct, and query multiple times until the answer is satisfactory.

This course teaches you how to architect systems where the LLM acts as a reasoning engine—dynamically routing queries, verifying its own answers, and orchestrating multi-step workflows.