Building production-ready RAG systems

Retrieval-augmented generation (RAG) is the default pattern for grounding large language models in your own data. A demo takes an afternoon. A system you can put in front of customers takes real engineering.

Start with retrieval, not the model

Most RAG quality problems are retrieval problems. If the right context never reaches the model, no amount of prompt tuning will save the answer. We invest early in:

• Chunking that respects structure — split on headings and semantic boundaries, not arbitrary token counts.
• Hybrid search — combine dense vectors with keyword search to catch exact terms that embeddings miss.
• Re-ranking — a cross-encoder pass to order candidates before they hit the context window.

Measure quality before you scale

You cannot improve what you do not measure. Before adding features, we build an evaluation set of real questions and expected answers, then track groundedness and relevance on every change.

A RAG system without evals is a guess with extra steps.

Keep the context window honest

// Trim retrieved chunks to a token budget before calling the model.
function fitToBudget(chunks: Chunk[], maxTokens: number): Chunk[] {
  let used = 0;
  return chunks.filter((chunk) => {
    if (used + chunk.tokens > maxTokens) return false;
    used += chunk.tokens;
    return true;
  });
}

Overstuffing the context window hurts both cost and accuracy. A tight, well-ordered context beats a large, noisy one every time.

Ship with guardrails

Production RAG needs answer validation, citations back to source documents, and a graceful fallback when confidence is low. These are not optional extras — they are what separates a trustworthy assistant from a liability.