RAG Architecture End to End

"How does the model know about our internal docs / our product / data from after its training cutoff?" is one of the first questions in almost every AI Engineer interview, and Retrieval-Augmented Generation is the default answer. It is also the topic where interviewers have the most room to probe, because everyone has heard of RAG and almost no one has watched a naive implementation fail in production. The gap between "I know what RAG is" and "I know where RAG breaks" is exactly what separates a pass from a strong pass.

A naive RAG system is not hard to build: chunk some documents, embed them, put them in a vector store, retrieve the top-k on a query, stuff them into a prompt, and generate. It works well enough in a demo to be convincing, and that is precisely the trap — it plateaus at "mostly right, confidently wrong sometimes" and stays there, because none of its failure modes are visible from the outside. The chunk that split a warning across a page boundary, the embedding model that changed between index-build and query-time, the retrieval call that returned five plausible-sounding but wrong passages, the prompt that buried the one relevant chunk under four distracting ones, the answer that cited nothing and could not be checked — all of these look, from the user's side, like "the AI got it wrong sometimes." Interviewers ask about RAG specifically to see whether you can name where "sometimes" comes from and what you would change at each stage.

This subject walks the full pipeline end to end — offline ingestion and online query — with one concrete failure mode per stage, the naive-RAG trap in detail, query understanding, the confidence gate, citations and grounding, and the advanced patterns worth naming without over-building. It assumes the RAG-vs-fine-tuning framing from llm-application-system-design and goes noticeably deeper on architecture and retrieval/generation mechanics than that subject's compressed RAG section. Two sibling subjects go deeper still on specific stages: chunking-and-embedding-strategies covers ingestion and chunking in depth, and vector-databases-and-hybrid-search covers indexing and retrieval internals — this subject references them rather than repeating their depth, but still covers the end-to-end architecture and the retrieval/generation stages thoroughly on its own. A third sibling, evaluating-rag-systems, covers how to measure everything described here. For a full worked design that puts RAG inside a real product, see case-study-customer-support-assistant.