Evaluating an AI Feature Before Launch
Build an evaluation set, grade outputs reliably and set launch gates your team agrees on
A taste of a lesson
Our feature scored 91 percent acceptable on 200 test cases. Is that good enough to launch?
The number alone cannot tell you. First, what were the 18 failures? If they are mostly awkward tone, that may be fine; if two invent refund amounts, that could be a blocker. Second, look by segment: maybe new users or long inputs fail far more often. Third, was 91 percent the threshold you agreed before testing, and do two graders agree on those labels? With 200 cases the true rate could plausibly sit a few points either side. Exercise: sort your failures into three severity groups and count each.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Build a representative evaluation set with tagged edge cases
- Write a rubric and measure agreement between human graders
- Use model graded evaluation with checks against human judgment
- Test safety, misuse, cost and latency before launch
- Set launch gates in advance and plan staged rollout and regression tests
Lesson plan
- 1 What the evaluation must decide Define the decision, the users and the risks the evaluation needs to cover. Start
- 2 Building the evaluation set Assemble real and edge case inputs that represent actual use. Start
- 3 Rubrics and human grading Grade outputs consistently with a clear rubric and agreement checks. Start
- 4 Model graded evaluation, carefully Use automated grading for scale while checking it against people. Start
- 5 Safety, misuse, cost and latency Test behaviour under attack and measure running cost and speed. Start
- 6 Launch gates and staged rollout Set thresholds before seeing results and plan a careful release. Start
Try asking
About this tutor
For product managers, tech leads and quality owners responsible for deciding whether an AI feature is ready to ship. You will build an evaluation set from real and edge case inputs, write a grading rubric, organise human grading with agreement checks, use model graded evaluation carefully, test safety and misuse cases, measure cost and latency, and set launch thresholds before you see the results. You also plan staged rollouts and regression testing for when prompts or models change. Lessons use your own feature where possible. You finish with an evaluation plan, a rubric, a first eval set outline and a launch readiness review template.
Reviews
4.7
3 ratingsSample
- Emeka O.Sample
Very solid. The safety lesson could be longer, but it pointed us to the right tests and we found a cross account leak in staging.
- Arjun P.Sample
Rigorous without drowning me in statistics. Tagging cases by segment showed our feature failed badly on non native English inputs, which the average had hidden.
- Ingrid S.Sample
Our team kept moving the goalposts after each test run. Setting gates in advance, with names next to them, ended that. The model grader validation lesson caught a grader that loved long answers.
About the teacher
Product management for AI features: deciding, specifying, testing and pricing them well
9 tutors 388 lessons taught Sample
I teach product managers and founders how to build AI features that people trust and keep using. I come from product work on software teams, where I learned that the hard part of an AI feature is rarely the model. It is deciding whether the feature should exist, writing down what good looks like, testing it before customers do and...
See Sanjana's profile and tutorsMore like this
Other tutors on the same or nearby topics.