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The AI UX Playbook

A research-backed framework for building AI products users trust. Built on Google PAIR and Microsoft's Human-AI Guidelines, plus my systematic review of the last decade of AI UX research.

Organized by the user journey - from launch to long-term use - this playbook translates academic UX research into practical design patterns you can apply today.
Initially

Before Building

These steps save costs and prevent avoidable rework later in the cycle.
Audit the dataset
Check your data for gaps, bias and licensing issues early so they never make it into the model.
Do you really need AI?
Check if AI truly beats rule-based systems for your problem before you spend time and money.
Initially

Onboarding

Show what the AI can and can’t do, and set clear expectations from the first click.
Make clear what the system can do
Sets realistic expectations so users know exactly what to ask the AI for.
Make clear that system may make mistakes
Calibrates trust by signaling uncertainty upfront.
Set expectations for adaptation
Tells users explicitly that the system will learn and evolve over time.
Initially

During Interaction

Give instant feedback, user control, and guardrails to prevent runaway errors.
Time services based on context
Delivers AI actions at moments that fit user task & environment.
Show contextually relevant information
Surfaces info aligned with current task/location.
Match relevant social norms
Uses language & tone appropriate for culture and setting.
Calibrate trust throughout the product experience
Design for trust in every step.
Balance control & automation
Gives users mode‑switches or sliders to keep agency where it matters.
Mitigate social biases
Actively avoids reinforcing unfair stereotypes or bias.
Initially

When Wrong

Own the mistake, explain it briefly, and offer a quick recovery path.
Support efficient AI help invocation
Lets users quickly summon AI help when desired.
Support efficient suggestion dismissal
Allows users to ignore or close AI suggestions easily.
Support efficient correction and Feedback
Enables quick editing or undo of AI output.
Be transparent and explain the error
Offers accessible explanations for AI behavior and decisions.
Provide paths forward from failure
Offers graceful fallback, manual override, or human help when AI fails.
Initially

Over Time

AI systems should learn and personalize over time while maintaining user trust through transparency, control, and feedback.
Remember recent interactions
Maintains short‑term memory for smoother multi‑step tasks.
Learn from user behavior
Personalizes experience by adapting to implicit user signals.
Update and adapt the model cautiously
Add changes gradually to avoid confusion and trust loss.
Encourage feedback and co-learning
Solicits lightweight, specific feedback that improves models over time.
Convey the consequences of user actions
Helps users make informed choices, reducing regret.
Provide global controls
Lets users adjust or opt‑out of personalization system‑wide.
Notify users about changes
Alerts users when capabilities or policies change, maintaining high trust.
Plan for co‑learning
Design touchpoints where user feedback teaches AI and vice‑versa.
Manage privacy & security transparently
Reduces legal risk and builds user confidence.

Building AI That Earns Trust at Every Step

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This site was designed and built by Tanja Radovanović.
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