The eval room: ship it?
The offline eval reads 98% and the team trusts it, but users keep hitting failures. Do you ship the next release or not?
The idea inside
A high offline score can be stale or contaminated, so it isn't enough on its own. Online evidence on fresh data, a red-team, and the feature's risk are what actually decide whether to ship.
After this lesson
You can decide whether to ship an AI feature on imperfect evidence: weigh offline vs online evals, eval-set freshness, red-team results, latency, and the feature's risk, instead of trusting one number.
Where it leads
Inside this lesson
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What this lesson shows
A high offline score can be stale or contaminated, so it isn't enough on its own. Online evidence on fresh data, a red-team, and the feature's risk are what actually decide whether to ship.
The question it opens with
The offline eval reads 98% and the team trusts it, but users keep hitting failures. Do you ship the next release or not?
The walkthrough, in the lesson's own words
- Good call. Roll the next release onto the bench.
- A release is on the bench. Read the signals and make the call.
- Work the bench: make the right call on at least 2 of the 3 releases.
- A high offline score is never enough on its own. Online, red-team, and risk decide it.
- You can't read the score off one number. A frozen offline eval can be stale or contaminated; online evidence on fresh data, a red-team, and the feature's risk are what tell you whether to ship.
- The team wants to ship because a public leaderboard number went up this week. What do you check before agreeing?
- Whether YOUR eval set, built from real user cases, moved. A leaderboard grades someone else's questions, not your users'. Then check that latency and cost held, and run the new model in shadow next to the real system before it takes over. Public benchmarks are not your users.
Key takeaway
You made the ship call on three releases with imperfect evidence, and saw why a frozen 98% is weaker proof than live results and a clean red-team.
What you can do after this lesson
You can decide whether to ship an AI feature on imperfect evidence: weigh offline vs online evals, eval-set freshness, red-team results, latency, and the feature's risk, instead of trusting one number.
This is the written summary. The lesson itself is interactive: you predict, drag and operate the mechanism above, and the reveal answers you.