AI Product Development
Turn model capability into a shipped, safe experience that survives contact with real users.
Strategy decides what to build; this track is about actually shipping it. The craft of AI products lives in evals, designing for the model being wrong, keeping a human in the loop where it matters, and getting a first version live. Start with evals, the most AI-specific skill of them all.
Guides and tools
Everything in the Shipping AI Products track. Live pages open now, more are on the build roadmap.
AI evals, explained
How to measure whether an AI feature is good enough to ship, and keep it good.
Read guide →Designing for AI failure
Confidence thresholds, fallbacks and honest failure the user can recover from.
In progressHuman in the loop
Where to keep a person in the flow, and where to let the model run.
In progressPrototyping with AI tools
Getting from idea to a working prototype fast with modern build tools.
In progressZero to one AI launches
Shipping a first version when you cannot fully predict behaviour.
In progressCommon questions
Frequently asked questions
What is the most important shipping skill?
Evals. If you cannot measure whether an AI feature is good enough, you cannot ship it responsibly or tell whether a change helped. Owning evals is the clearest way a product manager adds value on an AI product.
How is shipping AI different from normal software?
AI outputs are probabilistic, so you design for a range of behaviour and for the model being wrong. That means evals, fallbacks and monitoring for drift, on top of a normal launch process.
What should I read first here?
Start with AI evals explained. Evals underpin every other shipping decision, from setting a quality bar to catching drift after launch.