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The real user task is defined
Evaluation measures a concrete task rather than a vague notion of model quality.
AI evaluation
Plan evaluation around the real workflow
Discuss representative tasks, expected outputs, failure cases, human review and operational limits.
Prepare the conversation
Topics to discuss
Representative examples exist
Cases reflect normal inputs, difficult inputs and real edge conditions.
Acceptance criteria are explicit
Reviewers know what makes an output useful, incorrect, incomplete or unsafe for the workflow.
Source-backed answers are checked for faithfulness
When retrieval is used, evaluation verifies whether answers are supported by the retrieved evidence.
No-answer behavior is tested
The system can decline, escalate or ask for clarification when evidence or confidence is insufficient.
Sensitive-data cases are included
Tests cover privacy, confidential information and access-boundary behavior relevant to the deployment.
Tool/action requests are validated
If AI can call tools, tests include invalid parameters, duplicate actions, permission boundaries and failure responses.
Human review is defined where needed
The evaluation includes the actual review or approval step instead of assuming a person will notice problems.
Regression cases are retained
Known failures become repeatable tests so model, prompt or retrieval changes can be compared.
Latency is measured against the workflow
Response time is considered in the context of the user task rather than treated as an isolated benchmark.
Cost drivers are understood
Token usage, provider calls, retrieval and tool calls are measured without turning a demo estimate into a guarantee.
Provider and component failure is tested
The product has defined behavior for timeouts, invalid output, retrieval failure and unavailable dependencies.
Post-launch signals are identified
There is a plan for review outcomes, errors, user feedback or other useful quality signals after deployment.
Someone owns evaluation updates
A person or team is responsible for maintaining cases when data, models, prompts or product behavior changes.
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Use WhatsApp, phone or email to discuss these topics and agree the next step. Keep passwords, API keys and recovery codes out of your message.