Evaluating AI Output
A 45-minute discipline for professionals reviewing AI-generated work — spotting hallucinations, checking sources, building a verification habit.
Last updated: 2026-05-21
What you'll learn
By the end of this course you'll be able to:
- Why AI evaluation looks easy and isn’t
- The difference between accurate output and useful output
- Three hallucination patterns and how to spot each one
- How to evaluate sources and citations the model gives you
- How to spot bias in outputs — beyond the obvious cases
- A verification habit you can actually sustain past week two
Who this is for
Individual contributors and managers reviewing AI-generated work — their own, their team’s, or a vendor’s. Especially valuable in GCC, India, and Africa where multilingual outputs and regional context make verification harder than the typical English-only examples suggest.
Curriculum
7 chapters · 2 hands-on exercises · capstone challenge
Each chapter ends with the learning objectives ticked off. Quizzes are auto-graded with feedback; exercises are open-ended and produce artifacts you can take to your team.
1. Why AI evaluation is harder than it looks
- Identify the three reasons AI output feels more reliable than it is
- Recognize the "fluent but wrong" trap in your own reviews
2. Accuracy vs. usefulness
- Distinguish factual accuracy from task usefulness
- Apply the right test for each kind of AI output
3. Spotting hallucinations in 3 patterns
- Spot the confident-fabrication, plausible-detail, and stale-fact patterns
- Apply targeted checks for each pattern
4. Evaluating sources and citations
- Verify whether a cited source actually says what the model claims
- Avoid the fake-DOI and invented-paper traps
5. Spotting bias in outputs
- Identify three subtle bias patterns common in business outputs
- Apply a regional-context check for GCC, India, Africa, SEA
6. Building your verification habit
- Design a 5-minute verification routine you’ll actually keep
- Avoid the week-three drop-off in verification discipline
7. Making it stick: your verification playbook
- Draft a 1-page verification playbook for your function
- Lock in the 3 checks you will never skip on AI output
Interactive Course · Free
Full web-rendered experience available now.
All 7 chapters live with interactive slides, audio narration, mock-exam practice, and cross-device progress tracking. The first two chapters are accessible without an account.
References & sources
Built on cited sources — not vibes.
Every course is researched fresh against vendor documentation, regulatory sources, and peer-reviewed work. Sources used in this course:
NIST AI Risk Management Framework
National Institute of Standards and Technology · Source link
Stanford HAI — Trustworthy AI Research
Stanford Institute for Human-Centered AI · Source link
OWASP Top 10 for LLM Applications
OWASP Foundation · Source link
MIT Sloan Management Review — AI at Work
MIT Sloan · Source link
Course details
Track
Foundations
Level
Intermediate
Audience
Individual contributor, Manager
Industry
Cross-Industry
Stack
Stack-agnostic
Paired Gennoor Way phase
train, sustain
Format
video, reading
You finished the course. Now what?
From course to outcome.
Reading this course is step one. The next step is applying it where you work. Here's how Gennoor helps — without the deck, without the pitch.
Run this for your team
A 2-day workshop or virtual cohort for up to 25 of your people, with exercises run on your data and a 30-day adoption plan.
From $5k · 2 weeks · function-specific
Apply this to your data
A 4–6 week pilot that takes what you learned and ships a working system inside your environment. Fixed scope, fixed price, code transferred day one.
From $25k · 6 weeks · production-grade
Just want to talk?
Free 30-minute call. No deck, no pitch. We listen to your situation and tell you honestly what makes sense — even if it isn't us.
Free · no commitment · 30 minutes
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