How to Build a RAG Application (Retrieval-Augmented Generation)

RAG lets a language model answer questions using *your* documents — policies, docs, a knowledge base — without retraining the model. It's the most practical pattern for building AI that knows your business. Here's the whole pipeline.

The Four Stages

1. Chunk your documents into passages (a few hundred tokens each).
2. Embed each chunk into a vector and store it in a vector database.
3. Retrieve the most relevant chunks for a user's question via similarity search.
4. Generate an answer by giving those chunks to the LLM as context.

Indexing: Chunk and Embed

// Split docs, embed, and upsert into a vector store
const chunks = splitIntoChunks(document, { size: 500, overlap: 50 });
for (const chunk of chunks) {
  const vector = await embed(chunk.text);     // an embedding model
  await vectorDB.upsert({ id: chunk.id, vector, text: chunk.text });
}

Querying: Retrieve and Generate

const queryVector = await embed(userQuestion);
const top = await vectorDB.search(queryVector, { topK: 5 });

const context = top.map((c) => c.text).join("\n\n");
const answer = await llm.generate({
  system: "Answer ONLY from the context. If it's not there, say you don't know.",
  prompt: `Context:\n${context}\n\nQuestion: ${userQuestion}`,
});

What Makes RAG Good or Bad

• Chunking strategy matters more than the model — overlap and sensible boundaries prevent lost context.
• Ground the model hard: instruct it to answer only from retrieved context to reduce hallucination.
• Add citations so users can verify — return the source chunk alongside each answer.