HAX Design Library

Interactive collection of the 18 Guidelines for Human-AI Interaction, with design patterns for applying them and examples.

Guideline 2

Guideline 2

Make clear how well the system can do what it can do

Help the user understand how often the AI system may make mistakes.
Guideline 2 > Pattern 2A

Pattern 2A: Match the level of precision in UI communication with the system performance – Language
Guideline 2 > Pattern 2A > Example

Amazon Prime Video | 2A: Match UI communication precision with system performance — Language

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Guideline 2 > Pattern 2A > Example

Apple Music | 2A: Match UI communication precision with system performance — Language

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Guideline 2 > Pattern 2A > Example

LinkedIn | 2A: Match UI communication precision with system performance — Language

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Guideline 2 > Pattern 2B

Pattern 2B: Match the level of precision in UI communication with the system performance – Numbers
Guideline 2 > Pattern 2B > Example

Uber Eats | 2B: Match UI communication precision with system performance — Numbers

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Guideline 2 > Pattern 2B > Example

Zillow | 2B: Match UI communication precision with system performance — Numbers

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Guideline 2 > Pattern 2C

Pattern 2C: Report system performance information
Guideline 2 > Pattern 2C > Example

Contoso | 2C: Report system performance information

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Guideline 2 > Pattern 2C > Example

SimCam | 2C: Report system performance information

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Guideline 2 > Pattern 2C > Example

Gender Shades | 2C: Report system performance information

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Guideline 2 > Pattern 2C > Example

Dragon Professional | 2C: Report system performance information

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Guideline 2 > Pattern 2C > Example

Kayak | 2C: Report system performance information

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Guideline 2 > Pattern 2D

Pattern 2D: Provide low performance alerts
Guideline 2 > Pattern 2D > Example

Business Chat | 2D: Provide low performance alerts

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Guideline 2 > Pattern 2D > Example

Copilot in Outlook | 2D: Provide low performance alerts

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Guideline 2 > Pattern 2D > Example

Word Online | 2D: Provide low performance alerts

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Guideline 2 > Pattern 2D > Example

Google Maps | 2D: Provide low performance alerts

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Guideline 2 > Pattern 2D > Example

Microsoft Teams | 2D: Provide low performance alerts

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Guideline 2 > Pattern 9A > Example

Viva Topics | 9A: Switch classification decisions and G2

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Guideline 2 > Example

Hugging Chat | G2: Make clear how well a system can do what it can do 

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Guideline 2 > Example

Copilot in Word | G2: Make clear how well the system can do what it can do

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Guideline 2 > Example

Copilot in PowerPoint | G2: Make clear how well the system can do what it can do

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Guideline 2 > Example

Google Search | G2: Make clear how well a system can do what it can do

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Guideline 2 > Example

Google Bard | G2: Make clear how well a system can do what it can do

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Guideline 2 > Example

Hugging Chat | G2: Make clear how well a system can do what it can do

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Guideline 2 > Example

Business Chat | G2: Make clear how well the system can do what it can do

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Guideline 2 > Example

Copilot in Outlook | G2: Make clear how well the system can do what it can do

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Guideline 7

Guideline 7

Support efficient invocation

Make it easy to invoke or request the AI system’s services when needed.
Guideline 7 > Example

Copilot in Word | G7: Support efficient invocation

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Guideline 7 > Example

Copilot in Outlook | G7: Support efficient invocation

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Guideline 7 > Example

Excel Flash Fill | G7: Support efficient invocation

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Guideline 7 > Example

PowerPoint | G7: Support efficient invocation

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Guideline 7 > Example

Amazon.com | G7: Support efficient invocation

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Guideline 8

Guideline 8

Support efficient dismissal

Make it easy to dismiss or ignore undesired AI system services.
Guideline 8 > Example

Copilot in Outlook | G8: Support efficient dismissal

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Guideline 8 > Example

Copilot in Word | G8: Support efficient dismissal

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Guideline 8 > Example

Microsoft Forms | G8: Support efficient dismissal

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Guideline 8 > Example

Siri | G8: Support efficient dismissal

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Guideline 8 > Example

Instagram | G8: Support efficient dismissal

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Guideline 9

Guideline 9

Support efficient correction

Make it easy to edit, refine, or recover when the AI system is wrong.
Guideline 9 > Pattern 9A

Pattern 9A: Switch classification decisions
Guideline 9 > Pattern 9A > Example

Viva Topics | 9A: Switch classification decisions

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Guideline 9 > Pattern 9A > Example

Google Photos | 9A: Switch classification decisions

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Guideline 9 > Pattern 9A > Example

Gmail | 9A: Switch classification decisions

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Guideline 9 > Pattern 9B

Pattern 9B: Rich and detailed edits
Guideline 9 > Pattern 9B > Example

Copilot in PowerPoint | 9B: Rich and detailed edits

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Guideline 9 > Pattern 9B > Example

Notion | 9B: Rich and detailed edits

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Guideline 9 > Pattern 9B > Example

Copilot in Outlook | 9B: Rich and detailed edits

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Guideline 9 > Pattern 9B > Example

Copilot in Word | 9B: Rich and detailed edits

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Guideline 9 > Pattern 9B > Example

D365 Customer Service | 9B: Rich and detailed edits

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Guideline 9 > Pattern 9B > Example

Nichesss | 9B: Rich and detailed edits

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Guideline 9 > Pattern 9B > Example

LinkedIn | 9B: Rich and detailed edits

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Guideline 9 > Pattern 9B > Example

Google Maps | 9B: Rich and detailed edits

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Guideline 9 > Pattern 9B > Example

Apple Photos | 9B: Rich and detailed edits

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Guideline 9 > Pattern 9C

Pattern 9C: Undo automated actions
Guideline 9 > Pattern 9C > Example

Bing Search | 9C: Undo automated actions

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Guideline 9 > Pattern 9D

Pattern 9D: Do G9 through G15
Guideline 9 > Pattern 9D > Example

Instagram | 9D: Do G9 through G15

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Guideline 9 > Pattern 9D > Example

Google Photos | 9D: Do G9 through G15

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Guideline 9 > Pattern 9E

Pattern 9E: Batch-editing data
Guideline 9 > Pattern 9E > Example

Outlook | 9E: Batch-editing data

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Guideline 9 > Pattern 9E > Example

Google Photos | 9E: Batch-editing data

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Guideline 9 > Pattern 9E > Example

Gmail | 9E: Batch-editing data

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Guideline 9 > Example

Copilot in PowerPoint | G9: Support efficient correction

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Guideline 10

Guideline 10

Scope services when in doubt

Engage in disambiguation or gracefully degrade the AI system’s services when uncertain about a user’s goals.
Guideline 10 > Pattern 10A

Pattern 10A: Disambiguate before acting
Guideline 10 > Pattern 10A > Example

Copilot in Word | 10A: Disambiguate before acting

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Guideline 10 > Pattern 10A > Example

Github Copilot | 10A: Disambiguate before acting

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Guideline 10 > Pattern 10A > Example

iPhone | 10A: Disambiguate before acting

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Guideline 10 > Pattern 10A > Example

Google Search | 10A: Disambiguate before acting

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Guideline 10 > Pattern 10B

Pattern 10B: Avoid cold starts by eliciting user preferences
Guideline 10 > Pattern 10B > Example

Apple Music | 10B: Avoid cold starts by eliciting user preferences

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Guideline 10 > Pattern 10B > Example

Microsoft News | 10B: Avoid cold starts by eliciting user preferences

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Guideline 10 > Pattern 10C

Pattern 10C: Fall back to other strategies
Guideline 10 > Pattern 10C > Example

Box Skills | 10C: Fall back to other strategies

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Guideline 10 > Pattern 10C > Example

Kommunicate chatbot | 10C: Fall back to other strategies

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Guideline 10 > Example

Copilot in PowerPoint | G10: Scope services when in doubt

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Guideline 11

Guideline 11

Make clear why the system did what it did

Enable the user to access an explanation of why the AI system behaved as it did.
Guideline 11 > Pattern 11A

Pattern 11A: Local explanations
Guideline 11 > Pattern 11A > Example

AlphaCode | 11A: Local explanations

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Guideline 11 > Pattern 11A > Example

Google Search | 11A: Local explanations

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Guideline 11 > Pattern 11A > Example

Copilot in Excel | 11A: Local explanations

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Guideline 11 > Pattern 11A > Example

Business Chat | 11A: Local explanations

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Guideline 11 > Pattern 11A > Example

InterpretML | 11A: Local explanations

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Guideline 11 > Pattern 11A > Example

Pinterest | 11A: Local explanations

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Guideline 11 > Pattern 11B

Pattern 11B: Global explanations
Guideline 11 > Pattern 11B > Example

D365 Marketing | 11B: Global explanations

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Guideline 11 > Pattern 11B > Example

InterpretML | 11B: Global explanations

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Guideline 11 > Pattern 11B > Example

Instagram | 11B: Global explanations

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Guideline 11 > Pattern 11C

Pattern 11C: Present properties of system outputs
Guideline 11 > Pattern 11C > Example

Google Maps | 11C: Present properties of system outputs

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Guideline 11 > Pattern 11C > Example

Strava | 11C: Present properties of system outputs

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Guideline 11 > Pattern 11D

Pattern 11D: Map system input attributes to system outputs
Guideline 11 > Pattern 11D > Example

AlphaCode | 11D: Map system input attributes to system outputs

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Guideline 11 > Pattern 11D > Example

Gmail | 11D: Map system input attributes to system outputs

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Guideline 11 > Pattern 11D > Example

Sentiment Analysis | 11D: Map system input attributes to system outputs

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Guideline 11 > Pattern 11E

Pattern 11E: Map user behaviors to system outputs
Guideline 11 > Pattern 11E > Example

Netflix | 11E: Map user behaviors to system outputs

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Guideline 11 > Pattern 11E > Example

Goodreads | 11E: Map user behaviors to system outputs

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Guideline 11 > Pattern 11F

Pattern 11F: Example-based explanations
Guideline 11 > Pattern 11F > Example

Image recognition | 11F: Example-based explanations

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Guideline 11 > Pattern 11F > Example

Computer Vision | 11F: Example-based explanations

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Guideline 11 > Pattern 11G

Pattern 11G: “What if?” explanations
Guideline 11 > Pattern 11G > Example

Copysmith | 11G: “What if explanations”

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Guideline 11 > Pattern 11G > Example

Jasper.ai | 11G: “What if?” explanations

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Guideline 11 > Pattern 11G > Example

NerdWallet | 11G: “What if?” explanations

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Guideline 11 > Pattern 11G > Example

Grammarly | 11G: “What if?” explanations

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