A useful AI project starts with the workflow
Before choosing a model, understand the task, the person doing it, and the decision that follows.
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Source quality, access rules, and honest uncertainty matter as much as a fluent response.
Fluency is only the surface
An answer can sound clear and still be unsupported. In a business setting, people need to know whether an answer reflects the right document, the current version, and the circumstances in their question. Evaluating style alone misses the qualities that make an assistant useful in daily work.
Retrieval begins with the source material
Connecting a model to documents does not automatically create reliable knowledge. Teams need to identify authoritative sources, remove stale copies, preserve useful context, and decide how updates reach the system. Search quality depends on the structure and ownership of the information underneath it.
A citation should help someone check
A source reference is useful when it takes the reader to evidence that supports the specific claim. An unrelated link beside an answer creates the appearance of verification without the benefit. Test whether references are relevant, accessible to the user, and precise enough to inspect without repeating the entire search.
Make uncertainty an expected behavior
Sometimes the available sources do not contain an answer. The assistant should identify that limit, ask for useful context, or point to a responsible team. Include unsupported and ambiguous questions in evaluation so that the system is rewarded for an honest boundary as well as a helpful answer.
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