AI & Automation · Self-paced course
Evaluating AI Model Output
Judge whether AI outputs are good enough to use, with clear criteria, test sets and review methods.
$297
One-time payment · Self-paced course · 6 modules · 18 lessons
Enroll now — $297Read a free preview lesson
What this is
Learn to judge AI output systematically: defining quality, building test sets, writing scoring rubrics, automated and model-graded checks, human review and tracking quality over time. The course is organized into 6 modules — why evaluation, criteria, test sets, scoring methods, making decisions and ongoing monitoring — with 18 lessons in total, so you build the skill in a sensible order and can return to any part when you need it.
It does not promise that evaluation will catch every error, and it is not a research course on model benchmarking. It gives you practical methods to define quality, test outputs and decide when a human must check the work.
What you will be able to do
- Define quality criteria for a specific AI task
- Build a representative test set with expected answers
- Score outputs with rubrics, spot checks and automated checks
- Use model-assisted grading carefully and check its reliability
- Set thresholds for when output can be used and when it needs review
The curriculum
Module 1
Why evaluation
- Common AI output failures
- Defining good enough
- Evaluation as an ongoing process
Module 2
Criteria
- Accuracy, completeness and format
- Tone and policy requirements
- Writing a rubric
Module 3
Test sets
- Collecting real examples
- Edge cases
- Expected outputs
Module 4
Scoring methods
- Human review
- Automated checks
- Model-assisted grading
Module 5
Making decisions
- Comparing prompts and models
- Thresholds and sign-off
- Documenting results
Module 6
Ongoing monitoring
- Sampling production output
- Feedback loops
- Re-evaluating after changes
This is for you if…
- You use AI outputs in work that others rely on
- You have to choose between prompts, models or vendors
- You review AI-generated content and want a consistent method
- You lead a team adopting AI tools
What you get
- 18 lessons across 6 modules — the preview lesson is available now and the remaining lessons are released in stages
- A free preview lesson you can read before you buy
- One-time payment of $297 — no subscription
- Self-paced study with no deadlines or live sessions
- Practical steps you can apply to your own work as you go
Questions
Is a certificate, license or credential awarded?
No. This course does not award any certification, license, accreditation or professional credential. It is educational content to help you build practical skills.
Can I read a lesson before I buy?
Yes. One lesson is free to read — use the “Read a free preview lesson” button at the top of this page.
Are all the lessons available now?
Not yet. The free preview lesson is published now, and the remaining lessons are being released in stages. Enrolling gives you access to each lesson as it is published, at no extra cost.
How is the course delivered?
It is a self-paced online course. There are no deadlines, live sessions or set start dates; you work through the lessons when it suits you.
How do I pay?
Payment is a one-time charge through the Skill Stack Suite checkout. There is no subscription.
Do you promise any results?
No. The course is educational. It does not promise any income, earnings, job, promotion or business results — what you get from it depends on your situation and how you apply it.
Evaluating AI Model Output
$297
One-time payment · Self-paced course · 6 modules · 18 lessons
Enroll now — $297Educational content only. This course does not promise any income, earnings, revenue, job or business results; outcomes depend on your own situation and effort. No certification, license or professional credential is awarded.
Modules
Module 1: Foundations of Evaluation
Lessons
Module 2: Rubrics, Raters and Calibration
Lessons
Module 3: Evaluation Sets and Test Design
Lessons
Module 4: The Failure-Mode Catalogue
Lessons
Module 5: Automation, Judges and Metrics
Lessons
Module 6: Evaluation in Production
Lessons
Other Lessons
Writing a rubric
Free PreviewCommon AI output failures
Free PreviewBefore you can judge output you need to know what going wrong looks like. AI failures are not random noise; they fall into a small number of recognizable patterns, and learning to name them turns a vague feeling that something is off into a specific, checkable defect. Content Failures: Invented, Missing or Stale The most […]