An illustrative Flicker perspective. Replace with verified, attributed material before launch.
Name the decision you are improving
Describe the user workflow and the specific decision the feature supports. Identify the cost of a wrong answer and where human review belongs. A demo that impresses in a meeting is not the same as a feature that holds up under real, varied input.
Evaluate before you scale
Build a representative evaluation set early and measure reliability, latency and cost against it. Treat the model as one component in a system with fallbacks and guardrails, not as the whole product. Let the evidence, not the enthusiasm, decide the next investment.
Design the boundaries in
Decide what the feature is allowed to access, how its output is checked and how a person can override it. Clear boundaries are what let you ship AI into production without quietly taking on risk you cannot see.