Choose one bounded task
Start with a specific output, such as extracting a few approved fields or answering questions from a controlled document collection. Define when the system must decline or request review.
Check the data and permissions
Identify data rights, sensitivity, representative examples and missing or poor-quality records. Keep training or development examples separate from the final evaluation sample where applicable.
Establish a simpler baseline
Compare the proposed system with the current workflow, deterministic rules or conventional retrieval. The objective is a useful result, not a reason to use a model.
Evaluate errors, not just average scores
Use examples that include ambiguity, missing information and unsupported questions. Review which failures are costly, whether outputs are grounded, and what a person must verify before acting.
Include operating constraints
Track latency and cost under a realistic workload. Agree on access controls, logging boundaries, provider data handling, model changes and a fallback when the service is unavailable.
Set a decision gate
Agree in advance what evidence supports continuing, changing direction or stopping. A promising demonstration does not establish reliability across new data or justify removing human review.
A planning guide for discussion. Project commitments, acceptance criteria and operating responsibilities belong in an agreed scope.