Begin with a business task
Choose a narrow task whose outcome can be reviewed. For a knowledge assistant, that could mean helping a support colleague find an approved procedure and its source. Define acceptable behavior for missing, conflicting, or outdated information as carefully as for a successful answer.
Carry permissions through the workflow
An application must consider who is asking, which sources they may use, and what can be returned. Retrieval and downstream tools need the same scrutiny as the user interface. Plan data handling and deployment choices before connecting sensitive enterprise information.
Evaluate with representative work
Create a reviewed set of common tasks and difficult cases. Assess answer support, relevance, refusal behavior, and the usefulness of source references. Test whether an assistant recognizes when it does not have enough evidence. Re-run evaluation when the sources, model, or prompts change.
Keep actions bounded
A proposed action and an executed change are different responsibilities. Use explicit tool permissions, approval steps for consequential actions, audit records, and recovery paths. Do not interpret a fluent explanation as proof that an action is appropriate.
Assign operational ownership
Before release, agree who handles errors, updates the knowledge sources, reviews feedback, and monitors latency and cost. A production AI application is an ongoing system, with both software and process dependencies. These are design practices to assess per implementation, not a blanket compliance guarantee.
This editorial guide describes implementation considerations and illustrative approaches. It is not a customer case study or a claim of delivered capability.
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