AI·Jun 2, 2026·1 min read
RAG in production: grounding LLMs on your own data
Retrieval-augmented generation is how you make an LLM accurate, current and trustworthy. Here's how we ship it for real.
Large language models are powerful, but on their own they hallucinate, go stale, and can't see your private data. Retrieval-augmented generation (RAG) fixes all three by grounding the model on your own, current content at answer time.
Why RAG over fine-tuning
For most business use cases, RAG beats fine-tuning: it's cheaper, updates instantly when your data changes, and lets answers cite their sources so users can trust them.
What a production RAG system needs
- High-quality chunking and embeddings of your source content
- A retrieval layer tuned for accuracy, not just recall
- Citations so every answer is verifiable
- Evaluation and guardrails before and after launch
- Tracing and monitoring in production
Get those right and you move from an impressive demo to a system your team and customers actually rely on.