An AI assistant can be designed to find relevant information in approved business documents and use it to help answer questions. The useful outcome is quicker access to information, with clear sources and limits on what the assistant should do.
Organise the information first
Decide which documents are authoritative. Several versions of a policy, an outdated price list and an incomplete procedure can produce conflicting answers. Record ownership, review dates and who is allowed to access each type of information. One common approach retrieves relevant passages before generating a response. This is often called retrieval-augmented generation, or RAG. It can help connect an answer to your material, but it does not guarantee that the response is correct.
Useful requirements to agree
- Approved documents and update responsibilities.
- Access controls for different users.
- References showing where an answer came from.
- A clear response when information is missing.
- Human review for important or uncertain questions.
Start with a narrow set of questions, such as how to complete a standard internal process. Test the answers against the original documents before offering the assistant to a wider audience.
Questions and answers
Does this require building our own AI model?
Not necessarily. An existing model can be connected to an agreed information retrieval process. The design depends on your data, requirements and provider choices.
Will it only answer from our documents?
That needs to be specified and tested. Instructions alone are not a sufficient check; evaluate questions with missing, conflicting and unrelated information.
Can it respect staff permissions?
Access checks should be built into retrieval and the connected systems. A user must not receive restricted information simply because they asked the assistant for it.
Who keeps the answers current?
Your business needs an owner for the source information and a process for updating and rechecking it. The assistant cannot make outdated records authoritative.
Further reading: IBM explanation of retrieval-augmented generation.
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