Language model plus your own knowledge sources — and why quality hinges on preparation, not on the model.
Category · AI & Agents
Model + your own sources.
Retrieval-augmented generation pairs a language model with a knowledge base. On a query, the knowledge base is searched for relevant passages, those are handed to the model as context, and only then does the model generate its answer.
Upside: the model replies with current or company-specific information without retraining. And: answers come with citations, which reduces hallucinations and makes review possible.
What matters in practice.
The quality of a RAG system lives or dies with how you prepare the sources — chunking, metadata, reranking, evaluations. "Throw it into a vector DB and ship" rarely produces good results.
We use RAG for knowledge systems, product advisors, sales assistants, and technical documentation consumed by internal teams.
