Inside RAG - How It Really Works (Part 2/4)

RAG seems pretty straightforward at first glance. At the heart of it, the philosophy is, split your documents into chunks, search for the ones that match the question, feed them into the prompt, and finally, have the LLM generate an answer.

But the interesting parts live in the nitty gritty details of how that retrieval piece actually functions. There are four main stages: chunking, embedding, retrieval, and generation. Each one has far more depth and gotchas than the typical quick-start tutorial ever mentions. Let’s dive into the pipeline and look under the hood.

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Inside RAG — How It Really Works (And Why Most Projects Stall Before They Ship) — Things With AI

The gap between the RAG concept and production reality is where most projects quietly fail. Here's what the explanations usually skip.

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