Building Agentic RAG, Step by Step: From Static Retrieval to Reasoning Pipelines

Retrieval-augmented generation solved a real problem: it grounded LLM outputs in facts the model was never trained on. But classic RAG has a ceiling. It retrieves once, stuffs the results into a prompt, and hopes the top-k chunks happen to contain the answer. There's no self-correction, no multi-step reasoning, and no way to recover when the first...