Make AI useful in a workflow customers trust.

Define the task and the acceptable result

Write down the input, desired output, user, review process, and failure cases. Build representative examples before choosing a model or interface. Evaluate usefulness in the actual workflow rather than assuming a polished demo establishes reliability.

• What mistakes can the user detect and correct?
• When must the product ask for review or stop?
• Which data may be used and for what purpose?
• How will quality, latency, and cost be observed?

Separate evaluation from a showcase

Keep test cases with expected behavior and meaningful exceptions. Re-run them when the model, prompt, data source, or workflow changes. Record uncertain results rather than forcing every output into success or failure.

Choose relevant expertise

Seek product evaluation, domain, data, security, and deployment knowledge as the use case requires. The appropriate controls depend on the context; this hub does not establish regulatory compliance or professional suitability for an AI application.

Questions, answered.