Human Review That Scales
Set up human review of AI outputs that is consistent, affordable and feeds back into better systems.
A taste of a lesson
We only review answers users gave a thumbs down. Our failure rate looks terrible. Is the bot that bad?
Probably not as bad as it looks. Thumbs down answers are a targeted sample, chosen because someone was unhappy, so of course most look like failures. They are great for finding problems, not for estimating overall quality. For the overall rate, review a random sample of all answers, say one hundred, with the same rubric. Keep reviewing thumbs down separately to find failure patterns. How many answers does the bot give per week?
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
- Decide when human review is worth its cost
- Choose random, stratified or targeted samples for the right purpose
- Design review tasks and guidelines that keep reviewers consistent
- Calibrate reviewers and track agreement over time
- Turn reviews into metrics and fixes while protecting privacy and wellbeing
Lesson plan
- 1 When people should review Identify where human review adds value worth its cost. Start
- 2 Sampling that means something Pick samples that answer the question you are asking. Start
- 3 Designing the review task Build a review screen and rubric that keep reviewers fast and fair. Start
- 4 Reviewer calibration Keep different reviewers judging the same way. Start
- 5 From reviews to improvements Turn review results into metrics and fixes. Start
- 6 Privacy and wellbeing Protect the data being reviewed and the people reviewing it. Start
Try asking
About this tutor
For product teams, operations leads, domain experts and engineers who need people to review AI outputs, whether to measure quality, catch risky answers or validate automated graders. You learn when human review is worth its cost, how to sample outputs so results represent reality, how to design a review task and interface that keeps reviewers fast and consistent, how to calibrate reviewers and check agreement, and how to turn reviews into metrics and fixes. The tutor also covers reviewer privacy and wellbeing, especially when reviewing sensitive or harmful content.
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About the teacher
Evaluation for LLM products: eval sets, graders, regression tests, hallucination checks and live experiments
9 tutors 294 lessons taught Sample
I teach people how to know whether their LLM feature is any good, which is harder than building it. I started in software testing and quality work, moved into data analysis, and now help teams measure systems whose outputs vary from run to run. My lessons are about method: write down what good looks like, collect examples, grade them in...
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