Reviewing AI Support Answers for Quality
Build a sampling and rubric process to judge AI support answers and catch failures early
A taste of a lesson
Can we just use another AI model to grade all our support bot's answers?
It can help as a screen, especially for volume, but do not let it be the only judge. A grading model may miss the same things the bot misses, like an outdated policy that sounds plausible, and it tends to reward fluent answers. Use it to flag likely problems, then have people review a sample of both flagged and unflagged conversations, and compare its scores with human scores regularly. Keep humans on critical checks such as privacy and refund promises. Exercise: name two failure types you would never leave to automated grading alone.
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
- Define separate quality dimensions for AI support answers
- Write a rubric with critical pass or fail checks and anchored scales
- Design random and targeted sampling plans
- Calibrate reviewers and validate automated grading against humans
- Classify failures and route them to the right fix
Lesson plan
- 1 Defining a good answer Break support answer quality into distinct, checkable dimensions. Start
- 2 Writing the rubric Create a rubric with critical checks and anchored scoring levels. Start
- 3 Sampling plans Choose random and targeted samples that answer different questions. Start
- 4 Calibrating reviewers Make scores consistent across reviewers over time. Start
- 5 Automated grading with care Use model based grading as a screen and check it against people. Start
- 6 Failure types and fixes Classify failures and route each to the owner who can fix it. Start
- 7 The monthly quality report Report trends, harmful failures and fixes in a format leaders act on. Start
Try asking
About this tutor
For support quality leads, operations analysts and product or support managers responsible for AI answers reaching customers, whether from a chatbot, an email assistant or agent suggestions. You will learn to define what a good answer is for your support context, write a rubric that separates correctness, completeness, policy compliance, tone and safety, sample conversations sensibly, calibrate reviewers so scores mean the same thing, use automated grading with caution, classify failure types and feed findings back into knowledge base, prompt and routing fixes. You finish with a rubric, a sampling plan, a calibration routine and a monthly quality report format that shows trends and the most harmful failure types.
Reviews
4.7
3 ratingsSample
- Helena R.Sample
Separating critical pass or fail checks from tone scores transformed our reports. Leaders now see the two privacy slips instead of an average of 4.2.
- Amara O.Sample
Our automated grader turned out to miss outdated policy answers, exactly as warned. Validating it against humans is now standard for us.
- Diego C.Sample
The calibration routine fixed our reviewer disagreement problem within a month. Heavy material, so expect to do the exercises properly.
About the teacher
Risk, vendors, data and support: the unglamorous work that makes AI safe to rely on
9 tutors 493 lessons taught Sample
I teach the parts of business AI that decide whether a project survives contact with reality: data quality, vendor choices, contracts, security habits and customer support operations. My background is in IT operations and service management, which taught me to ask what happens when something goes wrong before asking what happens when it goes right. I teach with checklists, worked...
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