RAG Architecture
Retrieval-Augmented Generation (RAG) architecture explained: ingestion pipeline, vector search, prompt augmentation, and production patterns.
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RAG Architecture — Production Deep Dive
Almost every FDE scenario hides a RAG problem. And here is the uncomfortable truth: ~80% of RAG failures trace to ingestion and chunking, not the LLM. This section maps the whole pipeline so you know where to look.
RAG (Retrieval-Augmented Generation) turns a closed-book exam into an open-book one. Instead of hoping the model memorized your facts, you retrieve the relevant text at query time and hand it to the model as context. That means the quality ceiling is set by retrieval, not by the model — if the right chunk never makes it into the prompt, no amount of model intelligence will save the answer. Fix retrieval and you fix RAG.
Every RAG system is really two pipelines that meet at one place: a vector index. The offline pipeline runs in batch to build the knowledge base — parse, clean, chunk, embed, index. The online pipeline runs on every query — embed the question, search the index, rerank, and generate a grounded answer. Keeping these two straight in your head (and in the interview) is the whole game.