How to Build a Production RAG App (Step by Step)

RAG is the single most useful pattern in applied AI: it grounds a model in your data so it answers from facts, not vibes. Here is how to build one that survives real users, not just a demo.

The pipeline

1. Ingest — pull documents from your sources.
2. Chunk — split with structure in mind, not fixed character counts.
3. Embed — turn chunks into vectors.
4. Store — a vector database (pgvector works great) with good metadata.
5. Retrieve — vector search for the top-k relevant chunks.
6. Rerank — a second pass that keeps only the best few.
7. Ground — build a prompt from the retrieved context.
8. Answer with citations — always return sources.

A sketch

def answer(question: str) -> Answer:
    docs = retrieve(question, k=8)      # vector search
    docs = rerank(question, docs)[:4]   # keep the best
    context = format_context(docs)
    reply = llm.complete(SYSTEM, question, context)
    return Answer(text=reply, sources=[d.id for d in docs])

What makes it "production"

A demo stops at step 8. A production system adds:

• Evals — a test set + error analysis so you can prove quality.
• Observability — trace the question, retrieved context, tokens, and latency.
• Controls — retries, timeouts, caching, and a cost budget.
• Safety — input validation and prompt-injection defense.

See production-ready GenAI architecture for the full layer list.

Debugging RAG

When answers are wrong, check retrieval first. Log the retrieved context — was the right chunk even fetched? Fix chunking and reranking before you touch the prompt or model. This is also a favorite interview question.

Next

This is project one on the roadmap. Build it, put it on GitHub, and use it as your portfolio centerpiece — see 5 AI projects that get you hired.