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easyAI Engineering

What is RAG and why use it instead of fine-tuning for knowledge?

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01

Understand the problem

Retrieve-then-generate: fresh, cited, per-tenant knowledge without touching model weights.

ragretrievalfine-tuningarchitecture
02

Attempt it yourself

Sketch your approach before reading the solution — that's what interviews test.

Nudge consolestandby

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03

Study the solution

Retrieval-Augmented Generation retrieves relevant documents for a query (usually via embeddings) and puts them in the prompt so the model answers from them. Versus fine-tuning it wins for knowledge because it is updateable instantly (reindex, no retraining), auditable (citations), per-tenant scoped (retrieval filters e

Solution ready — 2 min read

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04

Read the code

Minimal RAG answer path
async function answer(question: string, tenantId: string) {
  const qVec = await embed(question);
  const chunks = await db.chunks.nearest(qVec, {
    where: { tenantId },            // access control lives HERE
    limit: 6,
  });
  const context = chunks.map((c, i) => "<doc id='" + i + "'>" + c.content + "</doc>").join("\n");
  return complete({
    system: "Answer only from the docs. Cite [doc-N]. Say so if not found.",
    user: context + "\n\nQuestion: " + question,
  });
}
05

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