Build Your First RAG

The six-step build: Every RAG system follows the same construction pattern regardless of scale. You will work through each stage — choosing documents, chunking, selecting an embedding model, storing vectors, querying, and generating answers. Understanding why each step exists is as important as knowing the code.

Step 1 — Choose Documents: Your RAG system can only answer questions about information it has seen. The documents you select become the system's entire knowledge base. In production, this could be thousands of PDFs, web pages, or database records.

Step 2 — Chunk: Documents are too long to embed as a single vector. We split them into smaller pieces so each chunk captures one focused idea, making search results more precise. A typical chunk is 40-100 words with 10-word overlap.

Step 3 — Embed: The embedding model determines how well your system understands meaning. Better models produce more nuanced vectors but cost more. Recommendation: Start with text-embedding-3-small — it is the best balance of quality, speed, and cost for most use cases (1536 dimensions, $0.02/1M tokens).

Step 4 — Store: Storing vectors in a specialized database (not a regular one) enables lightning-fast similarity search. An HNSW index is built automatically so searches take milliseconds, not minutes, even across tens of thousands of vectors.

Step 5 — Query: Your question is converted to a vector, compared against all stored chunks, and the best matches are returned. The similarity score (0-1) tells you how relevant each chunk is to your question.

Step 6 — Generate: The retrieved chunks are passed to the LLM as context, along with the original question. The LLM generates a grounded answer — because it has the actual documents, it gives specific, a