Agentic and Corrective RAG

Standard RAG retrieves once and generates; agentic RAG wraps retrieval in a loop so the model decides whether to retrieve, what to query, evaluates the results, and retrieves again until it has what it needs. Corrective RAG adds a grader that checks retrieval quality and takes corrective action (re-retrieve, web search, or discard) when the context is poor. Applied-AI interviews probe it because complex, multi-hop questions defeat single-shot RAG, and self-correcting retrieval is the fix, at the cost of more calls and agent-reliability concerns.