Building Your First RAG System: A Practical Architecture

A base model knows the public internet up to its training cut-off and nothing about your data. Retrieval-Augmented Generation closes that gap: instead of fine-tuning facts into the model, you fetch the relevant facts at query time and hand them to the model as context. It is the most practical way to put a language model to work on private, current, or domain-specific knowledge.

Why the model needs your data

Ask a raw model about your internal policies, your customer records, or last week's events and it will either refuse or confidently invent. RAG removes the guessing: the model only answers from what you retrieve, which makes its output groundable, citable, and current — three things a bare model cannot offer.

The pipeline: chunk, embed, retrieve, ground

Every RAG system is the same four steps. Split your documents into coherent chunks. Embed each chunk into a vector. At query time, embed the question and retrieve the nearest chunks. Then assemble those chunks into a grounded prompt and let the model answer from them alone.

// retrieve, then ground the answer in what you found
const hits = await store.search(embed(question), { k: 5 });
const context = hits.map((h) => h.text).join("\n---\n");
const answer = await llm.complete(
  `Answer ONLY from the context. Cite sources.\n${context}\n\nQ: ${question}`
);

The failure modes that bite first

Two things sink naive RAG. Bad chunking — splitting mid-thought so no single chunk carries a complete idea — wrecks retrieval before the model ever runs. And missing grounding discipline — letting the model answer without forcing citations — quietly reintroduces the hallucination you adopted RAG to escape. Fix the chunks, enforce the citations, and most of the magic takes care of itself.

RAG does not make the model smarter. It makes the model honest — it can only tell you what you were able to find.

This is the foundation the autonomous version builds on; see the Agentic RAG teardown for what happens when retrieval becomes a tool the model controls.