Building RAG Systems That Actually Work in Production

Retrieval-augmented generation (RAG) is the backbone of most useful AI products: it grounds a language model in your data so it answers from facts, not guesses. The demo is easy. Production is hard. Here's what separates the two.

The naive version breaks

A first RAG prototype usually looks like: chunk documents, embed them, retrieve the top results, stuff them into the prompt. It works in a demo and then fails when:

• Chunks are too big or too small.
• Retrieval pulls irrelevant context.
• The model confidently answers when it shouldn't.

What production RAG needs

1. Smart chunking

Respect document structure — headings, sections, tables — instead of blindly splitting by character count.

2. Hybrid retrieval

Combine dense vector search with keyword (BM25) search. Each catches what the other misses.

3. Reranking

A reranker reorders retrieved chunks by true relevance before they reach the model, dramatically improving answer quality.

4. Grounding & citations

Force the model to cite sources, and show users where answers came from. Trust is a feature.

5. Evals

You can't improve what you don't measure. A test set of real questions and expected answers turns "it feels better" into a number.

Reliability is the product

Anyone can wire an API call to a vector database. The hard, valuable work is making it reliable — accurate, observable and safe. That's the part we obsess over.

If you're considering RAG over your own knowledge base, we can help you do it right.