An AI assistant is only as useful as its context.

Define a narrow job

An assistant that helps a service team find approved product information has a clearer purpose than one expected to answer everything about the business. A narrow scope makes it easier to choose sources, evaluate responses, and explain limitations.

Write down the users, supported questions, unavailable information, and actions the assistant must not take. These boundaries should shape the interface as well as the technical design.

Curate the information

Retrieval-based approaches can supply relevant source material to a model. They do not automatically make the material current, correct, or appropriate for a particular user.

• Assign an owner and review cycle to source documents.
• Preserve useful titles, sections, and version information.
• Apply access rules before information is retrieved.
• Keep references available so people can inspect the source.
• Decide what happens when sources disagree or are missing.

Evaluate actual tasks

Build a set of representative questions with expected evidence and acceptable behavior. Include ambiguous requests, outdated information, questions outside the scope, and attempts to reveal restricted data.

Evaluate the whole system rather than the model alone. Retrieval failures, permission mistakes, unclear source material, and poor interface wording can all produce an unreliable experience.

Keep consequential actions reviewable

Reading a suggested answer is different from letting an assistant modify a customer record, approve a payment, or send a message. The greater the consequence, the more important explicit permissions, validation, and appropriate human confirmation become.

Make it possible to report a poor answer, inspect relevant context, and fall back to the underlying workflow. Do not describe generated output as guaranteed accurate. Trust grows from transparent boundaries and consistent behavior.