1. Be intentionally useful

Not every problem needs AI. AI should be a purposeful tool, not a gimmick.

• Does AI actually improve the experience, or is it just there for the sake of it?

• Does it solve a real user need better than a simpler, rule-based solution?

• If AI is required, does it add clear value in efficiency, accuracy, or user empowerment?

2. Guide intuitive inputs

AI is only as good as what it’s given. Users shouldn’t have to guess how to interact with AI; the system should show them the way.

• Does the system guide users toward quality inputs? (e.g., sample queries, tooltips, smart defaults)

• Is it clear what the user should—and should not—input?

• Are multiple input methods supported (text, voice, image, etc.) for accessibility and flexibility?

3. Guarantee quality outputs

AI responses must be reliable, understandable, and actionable. Users need to trust AI. Without trust, they won’t use it.

• Is the information accurate and trustworthy?

• Is the output clear and digestible, formatted to match user expectations?

• Does the system acknowledge uncertainty? (e.g., confidence scores, disclaimers)

• Is response speed balanced with accuracy? A lightning-fast response is meaningless if it’s wrong.

4. Be transparent about the system

AI should feel like a tool, not a black box. The more users understand how AI works, the more confident they’ll feel using it.

• Does the system explain how it arrived at an answer? (Especially important in high-stakes scenarios.)

• Are sources cited where possible?

• Is it clear what’s AI-generated vs. human-created?

• Does the system communicate its current status? ("Processing," "Generating," "Idle," etc.)

5. Give users autonomy

Users should always be in control. AI should empower, not dictate.

• Can users cancel, undo, or refine AI actions?

• Is there a way to provide feedback if something is wrong?

• Can users override AI decisions or take a manual path if needed?

• Are there clear settings for data privacy and usage?

6. Handle failures gracefully

AI will make mistakes. Design for that reality. Failure should be a learning moment, not a dead end.

• Does the system admit when it doesn’t know? (No hallucinated answers!)

• Do errors guide users toward better inputs? ("Try rephrasing," "Did you mean…?")

• Are partial responses handled well rather than failing completely?

7. Prioritize visual clarity

A good AI experience is a readable AI experience. Good design makes complexity feel simple.

• Is content easy to scan with clear hierarchy and visual emphasis?

• Is information presented accessibly, with proper contrast, spacing, and layout?

• Does the UI match the user’s mental model of how the system works?

8. Uphold ethical integrity

AI is powerful—but it must be responsible.

• Are privacy and security baked in, not an afterthought?

• Does the model minimize bias and potential harm?

• Can users control their data—how it’s stored, shared, or deleted?

• Is the AI accessible to users with different abilities and needs?

• Are limitations and biases disclosed upfront?