Every product team is shipping AI right now. Very few are shipping AI that customers trust enough to act on without double-checking. Building AI agents for product managers and working on AI security has taught me that trust is a product feature, and it has to be designed in from the start.
Ground every claim
If the model says something, the user should be able to see where it came from. In the agents I build, every quote is checked against the source document, and any claim without a valid source is dropped before the user sees it. Fewer answers, all of them verifiable, beat more answers that might be invented.
Compute numbers in code, not in the model
Counts, trends, percentages and totals should be calculated deterministically and handed to the model for explanation. A language model that "estimates" a metric will eventually be confidently wrong in front of an executive.
Build evaluations before features
An AI feature without an evaluation set is a demo. Before launch I want a labelled set of real examples, a definition of "good", and a score we track across every prompt, model or retrieval change. Your eval set becomes your moat; it is much harder to copy than a prompt.
Treat untrusted content as untrusted
Documents, emails, web pages and tickets can contain instructions aimed at your model. Wrap untrusted content clearly, tell the model it is data rather than instructions, and limit what tools an agent can call. Prompt injection is a product risk, not just a security footnote.
Design honest UX
Show confidence. Show sources. Make it easy to correct the AI and make corrections improve the system. Users forgive an AI that says "I'm not sure"; they don't forgive one that bluffs.
The goal isn't an AI that's always right. It's an AI whose mistakes are visible, rare and cheap.
The teams that win with AI won't be the ones with the flashiest demos. They'll be the ones whose customers stop double-checking.
