TokenMonsterA polished answer can hide an unresolved definition
Two teams can ask for revenue and expect different numbers. One means booked orders, another means invoiced sales, and a third means recognized revenue after adjustments. An assistant can produce a fluent explanation of any of them. Fluency does not resolve the disagreement.
Treat business definitions as part of the product specification. Name the metric, its owner, the filters that apply and the source that is authoritative. If a definition is contested, the application should expose that uncertainty instead of silently choosing whichever table is easiest to query.
Make the path to an answer inspectable
A useful analytics experience lets the reader understand where a result came from. Show the period, refresh time and relevant assumptions. Link to supporting records where permissions allow it. A source link is helpful only if it actually supports the claim next to it.
For a hypothetical inventory assistant, an answer about stock availability might need to distinguish warehouse inventory from units reserved for orders. A freshness label can explain why the figure differs from a live operations screen. These are product decisions, not small formatting details.
Carry access rules through every layer
The assistant should not gain a wider view simply because it sits between a user and a database. Decide which data each user can retrieve, how that restriction is enforced, and whether cached results could cross an access boundary.
Test with users who have different permissions. Ask the same question under each identity. Try a direct record request as well as a normal interface flow. The expected result may be a narrower answer, a refusal to retrieve a record, or a clear statement that the available information is insufficient.
Plan for changes
Data schemas, business definitions and access policies all evolve. Give those changes owners. Keep examples that demonstrate the expected interpretation and rerun them when a source or transformation changes.
Do not require users to understand your entire pipeline. Give them enough context to judge the answer and a clear route to report a problem. Behind the interface, preserve the evidence needed to investigate it.
Data quality is not a preliminary phase that ends before product development. It is an ongoing part of the experience you are promising to users.