AI Evals in a Weekend
Learn to measure whether your AI actually works - and prove it - in a single weekend
Your AI feature looks great in a demo, then does something weird for a real user - and you have no way to know how often, or whether your last tweak made it better or worse. This beginner-friendly course fixes that. In 18 short, screen-share lessons you'll learn evals: the simple discipline of measuring AI quality with evidence instead of gut feel. You'll map how your AI can fail, build your first eval set, use AI to grade AI, set a metric that tells you when to ship, and stand up a lightweight check that warns you when quality slips - all using tools you already have. You bring one real AI feature or assistant; you leave the weekend able to say, with numbers, whether it's good enough to ship.
with Steve Nouri
CEO, GenAI Works
Course Overview
Everyone can get an AI feature to work once, in a demo, on their own carefully chosen example. The hard part - the part almost nobody teaches beginners - is knowing whether it works for everyone, every time, and whether the change you just made helped or quietly broke something. That's what evals are for, and that's what this weekend is about.
You don't need to be an engineer or a data scientist. If you can write a prompt and open a spreadsheet, you can follow along. Every lesson pairs a short, plain-English explanation from your on-screen guide with a real build you watch happen step by step - no jargon dumped on you, no math you don't need.
You'll pick one real AI feature at the start - your GPT, your assistant, your app - and carry it through the whole course. By Sunday you'll have a small eval set, a metric that actually means something, and a simple way to catch problems before your users do. You finish able to answer the one question that used to make you nervous: 'Is it good enough to ship?'
What you'll learn
Understand why AI can't be tested like normal software - and why the same input can give different answers
Map how your AI feature can fail and turn 'it feels off' into a clear, structured list you can act on
Build your first eval set: a small collection of examples you can grade your AI against, again and again
Use AI to grade AI (LLM-as-judge) so you can check quality at scale without reading every output by hand
Pick one metric that tells you when to ship, hold, or roll back - and prove it actually tracks real quality
Stand up a lightweight pipeline that catches regressions and warns you when quality drifts in production
Make a confident ship / hold / roll-back call backed by numbers, and turn eval results into concrete fixes
Leave with a reusable weekend playbook you can run on any AI feature you build next
Meet the instructors
Steve Nouri is a distinguished data science leader who has transformed how the world perceives AI. He is the CEO and Co-founder of GenAI Works, the largest AI community with more than 14 million members missioned to Empower Society to Discover, Learn, and Grow in an AI-enabled World. Steve has made a significant impact in this field, frequently acknowledged as one of the top 10 global authorities in Artificial Intelligence and Data Science. As Australia's ICT Professional of the Year, Steve is a sought-after keynote speaker, educator, and technology influencer with a following of over 2 million people. He actively participates in and contributes to the Forbes Technology Council, ACM, Harvard Business Review, and numerous other prestigious organizations.
Who this course is for
First-Time AI Builders You've built a GPT, assistant, or AI feature and quietly worry it's unreliable - you want a simple way to check
Founders & Product People You're shipping AI in your product and need to answer 'is it good enough?' without a data-science team
AI-Curious Operators You use AI tools daily and want to move from guessing to measuring, with no coding background required
What's included
18 short, self-paced video lessons - about four hours, watchable in a weekend
On-screen builds you can follow step by step with your own AI feature
A starter eval-set template, LLM-as-judge prompt, and release-criteria checklist
A one-page reliability report template to share with your team
A reusable weekend eval playbook you can run on any future AI project
Course syllabus
18 lessons
Why 'It Worked in the Demo' Isn't Enough
Map How Your AI Can Fail
Good, Fast, Cheap: Pick Your AI Quality Bar
Log the Right Things (Without Drowning in Data)
Traces You Can Trust and Reproduce
Catch Regressions Automatically
Schedule
Format
Self-paced video course
Lessons
18 lessons
Price
$19
Level
Beginner
$19
One-time payment
- 18 short, self-paced video lessons - about four hours, watchable in a weekend
- On-screen builds you can follow step by step with your own AI feature
- A starter eval-set template, LLM-as-judge prompt, and release-criteria checklist
- A one-page reliability report template to share with your team
- A reusable weekend eval playbook you can run on any future AI project
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