Most businesses do not need to train their own AI model. They need an assistant that answers from their policies, manuals and product data. The common way to build that is called retrieval-augmented generation, or RAG.
How it works
Your documents are split into passages and indexed for search. When someone asks a question, the system finds the most relevant passages and gives them to the language model with the question. The model writes an answer based on those passages and can link to the source.
Why not just fine-tune a model?
Fine-tuning changes how a model writes, but it is a poor way to store facts that change often. With RAG you update a document and the assistant's answers change the same day.
Keeping answers accurate
- Show sources with every answer so people can check them.
- Tell the assistant to say “I don't know” when the documents do not cover a question.
- Test it against a list of real questions before launch, and review conversations weekly.
- Hand off to a person for anything sensitive, such as billing disputes or medical questions.
Privacy
Use business AI services that do not train on your data, limit which documents each user can search, and log access.
Our AI development and automation team builds document-grounded assistants with these checks. Try describing your idea in the AI Project Architect.