An AI Interface Style Guide

Do you recall the prolific web interface style guides that emerged during the mid-2000s up until the late 2010s? This was an era in which some might recall the prolific push of buzzwords such as “HTML5” and “CSS3”, alongside the emergence of UI frameworks like Bootstrap and Foundation. During this period we watched as the popularity and rationale behind various design standards rose and fell.

The release of Windows 8 in 2010 brought Microsoft’s “Metro UI” to the forefront of consumer software, featuring flat, colorful, square aesthetics organized into minimalist grids.Later, in 2014, Google published their UI design guidelines under the title “Material Design”. The primary angle for Material Design being the notion that the behavior of the user interface should resemble the behavior physical materials in the real world.

In more recent news (2025), Apple has introduced a controversial UI pattern in the latest version of its Mac and iOS dubbed “Liquid Glass”. While usability critiques seem to be a constant in design guidelines historically, the central truth is that there is genuine benefit from discussing and deliberating over established design and usability guidelines.

Perfectly ideal design guidance may never truly exist. As technology and society coexist and develop alongside one another the requirements and expectations of interfaces will continue to evolve.

Nothing can be said more of the advances and influences of technology than the significant advances in AI in the last few years. It is rapidly becoming apparent that the limitations imposed by past interface constraints are no longer present. Voice and video communication are becoming as easy to access as text. A fundamental change in the way we interact with software is just beginning to take shape.

So, let’s talk about designing for ideal user experiences in applications where the prolific availability of AI tools makes their inclusion in software inevitable. The guidelines that follow are based on my experience to date, building and using AI interfaces, and as I mentioned previously, I hope that they might face discussion and debate to help shape better designs in the future.

1. Summarization: When to Use It

Summarization is remarkably easy to build and add to most interfaces. This is often one of the first AI features that is pushed into apps to help brands put an “AI” label on their products in an attempt to look smart and cutting edge. The real truth is that there are only some cases where this functionality is useful. More often than not, poorly implemented summarization leads to a cluttered UI and leaves AI-tired users annoyed at the extraneous feature.

When Summarization Adds Value

Long-form content that users need to triage quickly: Email threads with dozens of replies, lengthy documentation, research papers, and meeting transcripts. These are ideal candidates because users need to decide whether to invest time reading the full content, and a summary helps them make that decision.

Repetitive information across multiple sources: When users are reviewing similar items (support tickets, customer feedback, news articles), summaries help identify patterns without requiring everything to be read verbatim.

Content in unfamiliar domains: Legal documents, medical records, or technical specifications often benefit from plain-language summaries that make specialized content accessible to non-experts.

When to Avoid Summarization

Critical information where accuracy matters: Financial statements, legal agreements, security alerts (anything where missing a detail has consequences should not be summarized by default). Users need to read the original.

Creative or emotional content: Poetry, fiction, personal messages. Summarizing these often strips away the very elements that make them valuable. The journey is the destination.

Already-structured content: If your content has clear headers, bullet points, and organization, it’s already scannable. Adding AI summarization is redundant.

Implementation Patterns

Generally, when you do implement summarization, consider progressive disclosure unless delivering a pre-generated result effects the delivery of the content in a beneficial way, such as a scenario where users would typically always choose to generate the summary:

# Good: Opt-in summarization
<article>
  <h1>{ article.title }</h1>
  <p class="meta">{ article.read_time } · { article.word_count } words</p>
  <button class="btn btn-sm btn-outline-secondary">
    <i class="icon-sparkles"></i> Summarize
  </button>
  <div class="content">{ article.body }</div>
</article>

# Bad: Summary forced on everyone
<article>
  <div class="ai-summary">{ ai_summary }</div>
  <details>
    <summary>Read full article</summary>
    <div class="content">{ article.body }</div>
  </details>
</article>

The first pattern respects user agency. The second assumes the AI knows better than the user what they want to read.

2. Chat Interfaces

Chat interfaces have become the default pattern for AI interaction, but they’re not always the right choice. Like command-line interfaces, they’re powerful and flexible, but they also require users to know what to ask for.

When Chat Works Well

Open-ended tasks: When users need to explore possibilities, iterate on ideas, or ask follow-up questions, chat excels. The conversational format supports the back-and-forth needed for refinement.

Complex queries: Natural language chat can be easier than filling out a form with 20 fields. “Find me a flight to Tokyo next month under $800 with a window seat” is more natural than navigating multiple dropdown menus.

Learning and exploration: When users are trying to understand a topic or troubleshoot a problem, the conversational format feels more supportive than static documentation.

Complex structured data: Complex data, such as time-series, row-based, and structured or hierarchical data can be useful to expose chat interfaces to help users quickly come to an understanding and explore trends and patterns within the data. Natural interfaces like these can be simpler to use than building out explicit queries, but ideally both types of interface will be exposed to support novice users alongside power users.

When Traditional UI is Better

Simple, repeated actions: If users need to do the same thing frequently (like setting a timer or checking the weather), dedicated UI elements are faster than typing the same request every time.

Precise inputs: Forms with validation, sliders, date pickers are constrained inputs that help users provide correctly formatted data. Chat often requires multiple exchanges to get structured data right.

Visual comparisons: Comparing products, reviewing options, or making choices between alternatives works better with side-by-side layouts than scrolling through chat messages.

Design Patterns for Better Chat

Start with suggestions, not a blank box: An empty input field is intimidating. Show example prompts or quick actions:

<div class="chat-suggestions">
  <button class="suggestion-chip">Help me draft an email</button>
  <button class="suggestion-chip">Summarize this document</button>
  <button class="suggestion-chip">Find similar articles</button>
</div>

This is especially important if you support more advanced functionality within your chat such as image generation, or other types of visual rendering.

Preserve context visually: Use visual hierarchy to show what the AI is responding to. Quote the user’s question in the response header, or maintain a sidebar showing the current conversation context.

Make AI limitations transparent: If your AI can’t handle certain requests, say so upfront. Don’t let users waste time discovering boundaries through trial and error.

Handle errors gracefully: When the AI doesn’t understand, offer specific ways to rephrase or switch to traditional UI:

I'm not sure I understand. Did you mean:
• Set a reminder for tomorrow
• Schedule a meeting
• Create a calendar event

Or would you prefer to use the calendar form?

3. When to use Voice

Voice interfaces are compelling. They’re hands-free, potentially eyes-free, and feel futuristic. But they’re also prone to errors, socially awkward in public spaces, and often slower than visual interfaces. Use voice where it genuinely helps, not just because you can.

Ideal Use Cases

Hands-busy, eyes-busy scenarios: Driving, cooking, exercising, and other times when users physically can’t interact with a screen. Voice is the only option that works.

Accessibility: For users with motor impairments, vision impairments, or reading difficulties, voice interfaces can be transformative. But ensure you’re building for actual accessibility needs, not just checking a box.

Quick, simple commands: “Set a timer for 10 minutes” or “Call Mom” work well with voice because they’re unambiguous and fast to speak.

When to Avoid Voice

Noisy environments: Coffee shops, open offices, busy streets, and anywhere background noise makes recognition unreliable or using voice socially uncomfortable.

Complex inputs: Entering passwords, addresses, or structured data by voice is frustrating. Spelling things out letter-by-letter is slower than typing.

Private information: Banking details, medical information, or personal messages should be avoided. Users often don’t want to speak these aloud, even at home.

Implementation Considerations

Always provide visual feedback: Even in voice-first interfaces, show what the system heard. Misrecognition is common, and users need to know if they were understood correctly.

Offer push-to-talk, not just wake words: Wake words (“Hey Assistant…”) lead to false triggers and constant listening concerns. Give users a button they can press when they want voice input.

Design multimodal fallbacks: When voice fails, seamlessly offer visual alternatives. Don’t force users to keep repeating themselves into the void.

# Example multimodal voice interaction
if speech_recognition_confidence < 0.7:
    return {
        "response": "I didn't quite catch that. Did you say:",
        "suggestions": top_3_interpretations,
        "fallback": "Or tap here to type your request"
    }

4. Auto-complete and Suggestions

AI-powered auto-complete and suggestions can accelerate user workflows or drive them mad with constant interruptions. The difference lies in timing, relevance, and control.

Suggestions That Help

Based on clear patterns: If a user always adds the same tags to certain types of documents, suggesting those tags saves time without being intrusive.

Completing tedious but predictable tasks: Email greetings, common responses, and boilerplate text are great candidates for AI completion.

When users pause: Suggestion timing matters. Show completions when users hesitate or stop typing, not while they’re actively composing.

Suggestions That Annoy

Correcting users who are right: If AI constantly suggests changes to correct spelling, terminology, or formatting, users will ignore or disable it. Be confident only when you’re actually confident.

Completing creative work: Writing a novel, composing music, or creating art. Users don’t want AI finishing their sentences. The creation process itself is the point.

Interrupting flow: Popping up suggestions while users are typing breaks concentration. Wait for natural pauses.

Give Users Control

Always provide an easy way to dismiss or disable suggestions. And make it truly easy, not buried in settings three menus deep:

<!-- Inline disable option -->
<div class="ai-suggestion">
  <span class="suggestion-text">Complete this sentence...</span>
  <button class="btn-dismiss">✕</button>
  <button class="btn-disable">Don't suggest for this field</button>
</div>

Having the Esc key dismiss suggestions can be a natural way to clear generated text recommendation when a keyboard is available.

5. Loading States and Progressive Responses

AI responses often take several seconds to generate. How you handle this waiting time dramatically affects perceived performance and user satisfaction.

Stream Responses, Don’t Buffer

When possible, stream AI-generated text as it’s created rather than waiting for the complete response:

// Stream tokens as they arrive
async function streamAIResponse(prompt) {
  const response = await fetch('/api/generate', {
    method: 'POST',
    body: JSON.stringify({ prompt })
  });

  const reader = response.body.getReader();
  const decoder = new TextDecoder();

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    appendToOutput(chunk); // Show immediately
  }
}

Streaming creates the impression of faster response and gives users something to read while waiting for completion.

Progressive Disclosure for Complex Queries

For tasks with multiple steps (research, analysis, generation), show progress:

✓ Analyzing document structure...
✓ Extracting key points...
⏳ Generating summary...

This transforms waiting from dead time into visible progress.

Set Expectations Upfront

If a task will take more than a few seconds, tell users before starting:

This analysis typically takes 15-30 seconds. 
[Start Analysis]  [Run in Background]

Let users choose whether to wait or continue working.

6. Transparency and Explainability

Users increasingly want to understand why AI makes certain suggestions or decisions. Building trust requires showing your work.

Show Confidence Levels

When AI makes predictions or suggestions, indicate certainty:

<div class="ai-classification">
  <strong>Category:</strong> Technical Documentation
  <span class="confidence confidence-high">95% confident</span>
</div>

<div class="ai-classification">
  <strong>Category:</strong> Customer Support
  <span class="confidence confidence-low">62% confident</span>
  <button class="btn-verify">Verify this classification</button>
</div>

Low-confidence results should prompt human review.

Cite Your Sources

When AI generates responses based on specific documents, data, or training materials, link to sources:

Based on the analysis, I recommend increasing the cache timeout.

Sources:
• Performance Report Q4 2025 (Page 12)
• System Architecture Documentation (Section 3.2)
• Similar issue resolved in Ticket #4521

This lets users verify information and builds trust in AI outputs.

Explain Decisions When It Matters

For high-stakes decisions such as content moderation, loan approvals, or medical suggestions it is important to provide explanations:

This message was flagged for review because it:
• Contains 3 prohibited keywords
• Matches known spam patterns (85% similarity)
• Sender has no previous message history

Review the message: [View Details]

Users need to understand automated decisions, especially when they can appeal them.

7. Graceful Degradation and Error Handling

AI will fail. Models will be unavailable, requests will timeout, and hallucinations will occur. Design for failure from the start.

Always Provide Fallbacks

Never gate core functionality behind AI that might fail:

# Good: AI enhancement with fallback
try:
    suggestions = ai_model.get_suggestions(context)
    return suggestions
except AIServiceError:
    # Fall back to rule-based suggestions
    return rule_based_suggestions(context)

# Bad: Hard dependency on AI
suggestions = ai_model.get_suggestions(context)
return suggestions  # Breaks if AI is down

Be Honest About Failures

When AI fails, admit it clearly and offer alternatives:

The AI assistant is currently unavailable.

You can:
• Use the manual search feature
• Browse by category
• Try again in a few minutes

Don’t show users cryptic error codes or pretend nothing is wrong.

Handle Hallucinations

Add disclaimers to AI-generated content:

<div class="ai-disclaimer">
  <i class="icon-info"></i>
  This summary was generated by AI. Please verify important details 
  in the original document.
</div>

Conclusion

The integration of AI into application interfaces represents a fundamental shift in how we design software, but it shouldn’t mean abandoning the principles of good interface design we’ve spent decades refining. AI features should enhance user capabilities, not replace user agency.

The most successful AI interfaces I’ve built and used share common characteristics: they’re transparent about their limitations, they offer users control and choice, they fail gracefully, and they solve real problems rather than applying technology in search of a use case.

As you design AI-powered features, constantly ask: “Does this make the user’s task genuinely easier, or does it just make our product look more advanced?” If you can’t articulate a clear user benefit, you’re probably building AI for AI’s sake.

The guidelines in this style guide will evolve as our understanding of AI interfaces matures. What works today may be superseded by better patterns tomorrow. But the core principle remains constant: design human interfaces for humans. The technology should serve the user’s goals, not the other way around.

As we move forward, I hope these guidelines spark discussion about what makes AI interfaces truly useful. Share your experiences, challenge these ideas, and help shape the next generation of design patterns. The best interfaces will emerge from thoughtful debate and iteration, not from blindly following any single style guide, even this one.