Enterprise knowledge assistant vs chatbot: what changes?

Separate conversation, retrieval and business actions

A chatbot is an interaction format. A knowledge assistant usually retrieves designated material before answering. RAG retrieves relevant content to support generation; it does not mean the model permanently learned every document or that every answer is correct.

References: Google Cloud：RAG architecture

An illustrative scenario: an expense-policy question

Suppose an employee asks about hotel reimbursement. General chat may describe common practices; an enterprise assistant should retrieve the applicable company policy and its effective date. Missing rules should be identified for the policy owner, not replaced with an invented amount. This is an illustrative scenario, not a client case.

Usable material matters more than document volume

Identify authoritative versions, owners, update methods and access boundaries. Resolve conflicting rules before integration and assess text extraction for scanned files. Document access should match employee permissions; uploading a file does not authorize everyone to read it.

What to review during acceptance

Test questions with known answers, questions lacking evidence and requests outside the user’s permissions. Check whether sources support conclusions, outdated policies are avoided and restricted information stays restricted. Our planning recommendation is to document these cases rather than rely on a few successful demonstrations.

When business tools become a separate requirement

Submitting an expense claim adds integrations, field validation, permissions and outcome confirmation beyond answering a policy question. Scope retrieval and execution separately; a successful Q&A demonstration does not establish acceptance of a business transaction.

A project checklist

• List authoritative material and its owners.
• Prepare answerable, unsupported and unauthorized questions.
• Scope retrieval and execution separately.

Common questions