Choose a bounded problem
Look for repetitive, measurable work with adequate data and a clear owner. Define success before selecting a model.
Evaluate with real examples
Build a representative test set and review output quality, latency, cost, and failure modes. Compare against a simpler non-AI baseline.
Keep people in the loop
Decide what needs human review and communicate limitations. Monitor performance and allow users to correct mistakes rather than treating model outputs as final.
General educational information; not legal, compliance, or security assurance.
Back to insights