AI Product Fundamentals
Non-determinism, trust, failure modes, UX for AI features
1
Trust-Through-Constraint Design
Constrain first, escalate by design, measure the model before you measure the business.
When you build an AI feature, users won't trust it just because it's impressive — they'll trust it when it feels predictable and safe. That means starting with low AI autonomy (suggestions only, human decides) and slowly giving the AI more control as you prove it works. Never let the AI do something high-stakes on day one.
2
Fault-Tolerant AI Design
Fluent output is not correct output — design the intercept before the user sees the wreckage.
When an AI makes a mistake, it doesn't crash or show an error — it just confidently gives you the wrong answer. Good AI product design means building safety nets so bad outputs are caught before the user sees them, and giving users easy escape hatches when the AI gets it wrong.
3
Fault-Tolerant UX Design
Design for your real error rate, not your ideal one — the UI is the safety net.
AI doesn't give the same answer every time, and it's sometimes wrong. So when you design an AI feature, you have to build in easy ways for users to say 'that's not what I wanted' and try again — otherwise every failure feels like a broken product. Think of it like a skip button on a radio: the DJ might play a song you hate, but if you can change it instantly, the experience still feels good.
4
Two-Layer AI Metrics
Completion, not clicks. Faithfulness, not fluency. Two layers or you're blind.
Most products measure whether users clicked or opened something. AI products need more: you also have to measure whether the AI's answers were actually correct and helpful. A user who clicked the AI button 20 times but still couldn't finish their task is a failure, not a success — so your metrics have to reflect completion, not just activity.
5
Deterministic Guardrails, Probabilistic Core
AI owns the ambiguous middle; deterministic logic owns the edges and the irreversible actions.
Traditional software follows exact rules: if this happens, do that. AI doesn't work that way — it makes probabilistic guesses, which means it can handle messy, open-ended inputs but can also be wrong in unpredictable ways. The key skill is knowing which parts of your product need the flexibility of AI and which parts need the reliability of hard rules — and designing clear handoffs between them.