Retrieval-augmented generation, grounded in your own data
Retrieval-augmented generation (RAG) is a pattern where a language model's answer is grounded in documents retrieved from your own data at query time, instead of relying solely on what the model memorized during training. A user's question triggers a search over your knowledge base, and the retrieved passages are fed to the model alongside the question, so the answer is traceable back to a real source.
Without retrieval, an LLM answers from general training data confident, fluent, and sometimes flatly wrong about your specific product, pricing, or policies. RAG is what makes an AI feature usable for anything where being right about your own business actually matters, and it's the difference between a chatbot that's a novelty and one a support team can trust.
We build RAG pipelines with the same rigor as the rest of an AI system: retrieval quality is tested independently of generation quality, and every model boundary gets input validation so retrieval doesn't become a new injection surface. It's core to how we approach the AI & ML development work described in our production AI case study.