A demo proves possibility
Prototype quality is usually judged by the best output. Production quality is defined by the entire distribution: routine answers, ambiguous inputs, failures, abuse, and recovery. Moving between those stages requires a measurable definition of acceptable behavior.
A useful evaluation set reflects real tasks and known edge cases. It should evolve alongside the product and run whenever prompts, models, retrieval, or policies change.
Control the system, not only the model
Reliable AI comes from system design. Scope the actions a model can take, validate structured outputs, protect tools with permissions, and make consequential steps reviewable. Treat model output as untrusted until the surrounding software verifies it.
Observability should capture enough context to investigate behavior without creating a new privacy problem. Clear retention rules and redaction are as important as detailed traces.
Design an honest experience
Users should know when AI is involved, what it can access, and how to correct it. Confidence comes from predictable controls and graceful failure, not from presenting uncertain output as certainty.