AI Product
Strategy, UX and economics of products built on probabilistic models.
Essays on AI Product
Human overrides should expire
A manual override can rescue a stuck workflow, then quietly become permanent policy. Give overrides an owner, a reason, and an expiry so exceptions return to deliberate review.
Make AI tools show their work before asking for trust
A fluent answer can hide missing evidence. Design AI interfaces to expose the source, the transformation and the uncertainty behind consequential outputs, so users can verify the part that matters.
AI product hypotheses as experiments, not roadmap features
When we treat an AI feature as a fixed requirement, we lose the ability to learn. Framing them as hypotheses allows the team to pivot based on real-world reliability.
The fallback is part of the AI experience
When an AI answer is uncertain, a blank error state leaves users stranded. Design the fallback as a clear next step, with boundaries and a useful alternative.
Audit your RAG chunks before you tune the model
If retrieval hands the model the wrong passage, no prompt or model swap can recover the answer. Grade your chunks before you tune anything downstream.
Design for the answers your model gets wrong
Most AI UX is designed around the demo, where the model is right. The product people trust is designed around the moments it isn't. Five patterns that help.
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