Useful technology emerges from clear decisions about context, responsibility and the way people work. Three considerations for ai engineering.
Choose representative tasks
An evaluation set should reflect real input variety, ambiguous requests and known failure conditions. Easy examples alone can hide the gaps users encounter.
Measure more than an answer
Review source use, tool behavior, latency and escalation paths as well as response quality. The complete workflow determines whether the product is useful.
Keep learning after release
Collect structured feedback and review failure patterns with an owner. Changes to prompts, retrieval or models should be tested against the same meaningful tasks.
Start with a workflow you can understand, observe and improve.
These are general design considerations. Implementation depends on the system, users and operational requirements.
Discuss your context