AI evaluation belongs in the product conversation

AI features are often discussed as if prompt quality were the main constraint. It rarely is. Most product failures around AI happen earlier, when nobody has defined what acceptable behavior looks like, how errors should degrade, or what signals prove the feature is actually helping users.

Evaluation has to be part of product design, not a technical afterthought. That means deciding which outputs matter, which failure modes are tolerable, where human review is required, and how usage will be measured once the feature is live. Without that framing, the team is not improving a system. It is iterating blindly on a demo.

A stronger prompt can improve a narrow failure case, but it cannot replace product decisions about trust, latency, reversibility, privacy, or user expectation. Those choices define whether the feature belongs in production at all.

The practical path is to treat AI output like any other operational dependency. Define the contract, design the fallback behavior, observe it in real use, and improve it with evidence. That keeps the conversation grounded in product value instead of model mystique.