RAG development for knowledge assistants that answer from your documents

RAG stands for retrieval-augmented generation. The system searches your documents for passages that relate to a question, hands them to a language model and asks it to answer only from them, with links. The model supplies the wording, and your documents supply the facts. Our RAG guide explains chunking, retrieval and testing in more detail.

An enterprise knowledge assistant answers staff or customer questions across your internal sources: policies, contracts, tickets, wikis and databases. It differs from a search box in that it writes a short answer and cites where it came from. It differs from a public chatbot in that it respects each person’s permissions and stays inside the content you connected.

RAG looks up your documents at the moment a question is asked, so answers stay current and cite sources. Fine-tuning changes the model itself by training it on examples, which suits a fixed task such as classifying tickets. For questions about company knowledge that changes every week, RAG is almost always the better first choice.

ChatGPT uses retrieval when it searches the web or reads files you upload, but it does not know your internal documents or your access rules. A RAG knowledge assistant is connected to your own sources, keeps them in sync and applies each person’s permissions.

Answers are generated only from retrieved passages and cite them. When the sources do not support an answer, the system is designed to decline, and we measure how often it declines correctly on a set of your own questions before launch.

Yes. Access rules from your source systems are applied at retrieval time, so people see answers only from content they can already open.

In your cloud account or a dedicated environment we manage, depending on your requirements. Your content is never used to train models for other clients.