evaluation

Summaries shape purchases strongly, so their framing matters. Summarise only themes several verified reviewers share, show counts and the balance of views, link to the source reviews, and test for sentiment drift before release.

Users rate flattering answers higher, so the launch metric points the wrong way. Build the measurement that catches it and decide what blocks a launch.

Reasoning tokens are billed as output and add latency. Find the lowest effort that holds quality for each feature, and spend high effort where it measurably helps.

Constraining output format helps parsing and can hurt thinking. Decide where the reasoning happens, then test the structure on your own eval.

Translation through English helped low-resource Indian languages and hurt Hindi in one benchmark. Route by language, protect what must not be translated and measure on your own customers' messages.

Define what unread means, preserve edits and permissions, and make every action item traceable. The design must tell users when the summary covers only part of their inbox.

Open schema access answers more questions and produces more wrong numbers that look right. Choose the scope, the permissions and the evidence each answer must show.

The token bill drops by two-thirds and failures rise by one point. Price the failures before you call it a saving, and check what routing does to your cache.

Stuffing every past conversation into context is expensive and still forgets. Decide what becomes a memory, how deletion cascades and how you test updates and forgetting.

Models rarely ask unless told to, and research shows asking at the right moment raises success substantially. Ask when the answer changes the outcome and the action is costly, batch questions up front, and measure both kinds of error.

Published systems measure whether comments lead to code changes, aim for a precision target and filter non-actionable comments. Compare against human comments, seed known bugs for recall and run a randomised rollout.

A launch question where positioning, pricing, evaluation and capacity all have to line up. The interviewer wants to see you avoid cannibalising the wrong tier and give developers what they need to adopt it.

Quantization offers large savings with small average quality loss, and the averages hide where the loss lands. The interviewer wants evaluation on real workloads, by segment, before any cost win is claimed.

Model documentation is read by developers deciding whether to build on a model, by enterprises doing due diligence, and by regulators. The interviewer wants contents that serve all three, honestly.

Prototypes tested in English hide most of what goes wrong for users in other languages. The interviewer wants a prototype cycle that tests real speech and text from target users, with native evaluation.

Two minutes rewards one finished user journey and an honest failure. Confirm the AI-tool rules first, build one end-to-end job with a small eval, and spend the video on the problem, the demo, the failure and the next step.

Panels want to hear that you did not average two signals. Show how you found out which one was measuring the wrong thing, and what you added to the launch gate afterwards.

Fixing harmful outputs often creates over-refusals, and fixing bias can create absurd answers. Show how you found the second problem and the paired metric you added so it would not happen again.