Agentic and Corrective RAG

Standard RAG retrieves once then generates; agentic RAG puts retrieval in a loop so the model chooses whether to retrieve, what to query, judges the results, and retrieves again until it has enough. Corrective RAG bolts on a grader that inspects retrieval quality and reacts (re-retrieve, web search, or discard) when the context is weak. AI, ML, and GenAI engineer interviews test it because complex, multi-hop questions beat single-shot RAG, and self-correcting retrieval is the remedy, paid for in extra calls and agent-reliability concerns.