Three-hour AI evals workshop for product teamsMake AI qualitya team habit.
In three hours, your PMs, domain experts and engineers build an AI feature together, agree what good looks like, find where it breaks and prove what changed. Everyone leaves knowing their part in the loop, and the team leaves with a way of working it can repeat on every release.
Trusted by teams shipping AI in production
For teams at any stage of building with AI.
“How should my team approach AI evals?” 64% of people at our last workshop came with this question.
Before you build
- I'm still a skeptic. Show me that AI really works.
- Which model gives us consistent answers, fast?
You're scoping your first AI feature and want the quality bar agreed before anyone writes a prompt.
Before you ship
- We picked a model and never compared it.
- We do manual checks before each release.
You have a working version and need evidence it's ready before it reaches users.
After you ship
- “How's your AI doing?” “Good question.”
- We upgraded the model. Then the complaints started.
Your feature is live, nobody can say how good it is, and the improvements keep sliding down the roadmap.
The quotes are what product teams and builders tell us most often, in interviews and at our workshops.
One AI feature, the whole team in the loop.
In three hours your team takes one AI feature from a first prompt to a version you can prove is better.
A ready-made AI product everyone in the room can judge.
Your own AI feature, planned with you on a call.
- Step 1
Write the prompt and the test cases.
Clear instructions for the feature, plus test cases from easy to tricky.
- Step 2
See multiple models results.
Same prompt, same tests, several models. Compare the answers blind.
- Step 3
Find where it breaks.
Review the outputs together and group what goes wrong into patterns.
- Step 4
Add evals, fix and run again.
Turn the patterns into evals, then rerun and compare with the first run.
What your team leaves with.
- 01
A framework to run on your own product
The same steps work on your next prompt change and your next AI feature. Before you leave, you pick the first experiment to run on your own product.
- 02
A rubric the whole team agrees on
Written together from the outputs in the room, so measuring AI quality stops depending on who happens to look.
- 03
A tested system prompt
Built from a product requirement and checked against realistic test cases, not tried once and shipped.
- 04
A baseline across models
Accuracy, cost and latency for each model you tested. The next model decision has numbers behind it.
- 05
A map of failure modes
Annotated outputs grouped by how the feature breaks, ordered by what to fix first.
Excellent course for product managers looking to move beyond intuition. Clear, practical, and immediately applicable.
Learned way more than I expected, especially how to actually prove a prompt got better instead of just guessing.
Incredibly helpful. The course introduced the topic in a very easy to learn way, but with lots of key takeaways, blending interactive components with theoretical understandings. Would highly recommend!
An insightful and very well structured session, would definitely recommend to other friends and colleagues interested in understanding how to build AI products.
What happens after the workshop.
No commitment. You keep everything you built in the workshop, whether or not you want to continue working with us afterwards. Still, if you want to work together and keep in touch, here are some of the options:
A three-week activation sprint
We work on one of your AI features with you for three weeks, from eval setup to measured improvements. Results from our work with client teams:
Let's find a date for your team's AI evals workshop.
Send us a few details and we'll propose dates for your team. If you'd like the workshop built around your own AI feature, we'll start with a 30-minute call to understand your use case.
Tell us about your team.
FAQ,
briefly.
Still on the fence? Here's what most teams ask before their first eval run.
