The Act / Review / Ignore rule
One score, one action. An unexplained 87% is a shrug with decimals — it tells the user how the model feels and nothing about what to do. Every AI output should resolve to an explicit next state; in the products I’ve shipped, three verbs covered the production cases: act, review, or ignore.
loop.js
Four of these patterns exist as a zero-dependency JavaScript module, so the decisions can be tested instead of debated: which verb a score becomes, whether its confidence has earned belief (Brier score, expected calibration error), when to refuse to answer, and what to show while the model thinks. Forty-two assertions, running in your browser.
Confidence Score Patterns
An unexplained 87% is a shrug with decimals. A score earns its pixels only when it resolves to a verb.
AI Failure States
Users forgive a model for being wrong. They never forgive it for bluffing.
ML Explainability
A recommendation you can trace, you’ll defend. One you can’t, you’ll quietly rebuild around.
Human-in-Loop Patterns
An override is not defiance. It is the training data the next version needs.
Provenance & Citations
A claim with its source beside it is evidence. The same claim without one is prose.
The Capability Contract
The honest “no” is what makes the confident answer believable.
Calibration & Track Record
“80% sure” is a promise. Show whether it has been kept.
Reversibility
People don’t act when the model is right. They act when being wrong is cheap.
Google’s PAIR guidebook (opens in a new tab) and Microsoft’s HAX toolkit (opens in a new tab) map this territory from the research side — read them; they’re excellent, and several patterns here have cousins there. This library doesn’t compete with the lab. It’s the production-side report: what those guidelines don’t tell you about week three, when users have stopped reading the explanation chips and started gaming the confidence threshold.
What’s distinctly mine: the Act / Review / Ignore rule — a score never reaches the screen without resolving to a verb; abstention treated as a shippable feature, not an error state; the override captured as structured data the system learns from; and every pattern here priced by a product that actually shipped. Each page also says when not to use the pattern — the discipline that separates a reference from a sales pitch.
Maheshwari, A. (2026). A Field Guide to Trust: AI Design Patterns. arpitmaheshwari.com/patterns/.
Prefer it as a book? The same patterns run as live demos in the field guide →