Beyond Text-Only AI: On-Demand UI Generation for Better Conversational Experiences

In today’s AI-driven world, we’re increasingly interacting with sophisticated language models through simple text interfaces. While powerful, this raw text communication paradigm introduces significant user experience challenges that limit accessibility, efficiency, and ultimately user satisfaction.

What if AI systems could generate precisely the right interface components when needed, without requiring developers to build every possible UI variation in advance? This post explores my work on a new prototype that enables LLMs to create dynamic, interactive UI components on demand, transforming how users interact with AI systems and MCP servers.

The Problem with Raw Text Interactions

Text-based interactions with AI have several inherent limitations:

1. Cognitive overload: Users must translate their structured needs into unstructured text
2. Ambiguity: Natural language is inherently ambiguous and prone to misinterpretation
3. Input validation: No guardrails for ensuring correct data formats or required fields
4. Accessibility barriers: Text-only interfaces exclude or complicate use for certain disabilities
5. Efficiency bottlenecks: Typing complex instructions is slow and error-prone
6. Context management: Text threads quickly become unwieldy for complex tasks
7. Visualization limitations: Some information is best understood visually, not textually

For enterprise applications, customer service scenarios, and complex workflows, these limitations become even more pronounced. Consider a shipping company’s customer support system—asking users to provide complex shipping details, tracking numbers, or address information through pure text is frustratingly inefficient.

The Promise of AI-Generated UI Components

The solution lies in enabling AI to dynamically generate appropriate UI components based on the conversation context. When a shipping company customer needs to change their delivery address, instead of a cumbersome text exchange:

Bot: What's your new address?
User: 123 Main Street
Bot: What city?
User: New York
Bot: What's the ZIP code?
User: 10001
Bot: What state?
User: NY

The AI can generate a complete address form that collects all required fields at once, validates the input, and provides appropriate feedback—all while maintaining the conversational nature of the interaction.

This approach bridges the gap between conversational AI and traditional application interfaces, giving users the best of both worlds: the flexibility and naturalness of conversation with the precision and efficiency of structured inputs.

How LLM-UI Generation Works

The mechanism for generating UIs from LLMs follows this general pattern:

1. Request interpretation: User makes a request through natural language
2. Intent recognition: LLM identifies the task and required data points
3. Component selection: LLM determines appropriate UI component(s) for the task
4. Component generation: LLM generates structured JSON defining the UI component
5. Rendering: Application renders the component alongside conversational response
6. Interaction handling: User interacts with the component; data is collected
7. Structured processing: The collected structured data is processed by the AI or backend system

From a technical standpoint, the LLM generates a JSON specification that describes the desired UI component, which the client application then renders using its component library.

Integration with MCP

The AI-generated UI approach pairs exceptionally well with MCP services. As discussed in my previous post about MCP tools, MCP provides standardized methods for communicating with MCPs. By extending MCP servers to accept structured data from UI components, we create a powerful ecosystem for human-AI interaction.

This integration offers several benefits:

1. Standardized communication: UI components provide a consistent way to collect and validate inputs for MCP services
2. Reduced cognitive load: Users interact with familiar interface patterns rather than learning service-specific text commands
3. Enhanced capabilities: Complex MCP services become more accessible through purpose-built interfaces
4. Data validation: Input validation happens at the UI level before data reaches MCP services
5. Seamless experience: Users don’t need to understand the underlying MCP architecture

For example, when a user wants to access a filesystem MCP service (as described in my filesystem MCP server post), instead of seeing raw MCP outputs, the AI can generate an intuitive file browser UI component with familiar folder icons, breadcrumb navigation, and drag-and-drop capabilities - providing a natural way to browse and manage files without requiring text commands.

UI Component Types for AI Systems

Through extensive testing, I’ve identified several component types that provide exceptional value in AI-generated interfaces:

1. Forms

Forms are the workhorse of structured data collection. They enable simultaneous collection of multiple related data points with appropriate validation. Forms are ideal for:

• Personal information collection
• Address entry/updates
• Product/service configuration
• Survey responses
• Application submissions

Example form JSON structure:

{
  "type": "form",
  "data": {
    "id": "address-change",
    "title": "Update Delivery Address",
    "fields": [
      {
        "id": "street",
        "label": "Street Address",
        "type": "text",
        "required": true
      },
      {
        "id": "city",
        "label": "City",
        "type": "text",
        "required": true
      },
      {
        "id": "zip",
        "label": "ZIP/Postal Code",
        "type": "text",
        "required": true,
        "validation": "^[0-9]{5}(-[0-9]{4})?$"
      },
      {
        "id": "country",
        "label": "Country",
        "type": "select",
        "options": [
          {"label": "United States", "value": "us"},
          {"label": "Canada", "value": "ca"},
          {"label": "United Kingdom", "value": "uk"}
        ],
        "required": true
      }
    ],
    "submitLabel": "Update Address"
  }
}

2. Selection Components

Decision-making interfaces help users make choices from defined options:

• Buttons: For simple binary or multiple-choice selections
• Radio buttons: For mutually exclusive options
• Checkboxes: For multiple selections from a set
• Dropdown menus: For space-efficient selection from many options
• Sliders: For selecting values along a continuum

Example button component:

{
  "type": "buttons",
  "data": [
    {"label": "Confirm", "value": "confirm", "variant": "primary"},
    {"label": "Reschedule", "value": "reschedule", "variant": "secondary"},
    {"label": "Cancel", "value": "cancel", "variant": "danger"}
  ]
}

3. Data Visualization Components

For presenting structured information clearly:

• Tables: For comparing data across rows and columns
• Lists: For presenting sequential or grouped information
• Cards: For cohesive presentation of related data points
• Progress indicators: For showing completion status or timeline position
• Badges: For status indicators and category labeling

Example table component:

{
  "type": "table",
  "data": {
    "columns": ["Shipping Option", "Price", "Estimated Delivery"],
    "rows": [
      {"Shipping Option": "Standard", "Price": "$4.99", "Estimated Delivery": "5-7 days"},
      {"Shipping Option": "Express", "Price": "$12.99", "Estimated Delivery": "2-3 days"},
      {"Shipping Option": "Next Day", "Price": "$24.99", "Estimated Delivery": "1 day"}
    ],
    "caption": "Available shipping options for your order"
  }
}

4. Complex Composite Components

For more sophisticated interactions:

• Multi-step wizards: For breaking complex processes into manageable steps
• Calendar selectors: For date/time selection
• File uploaders: For document and media sharing
• Rating interfaces: For collecting feedback
• Map selectors: For location-based inputs

Real-World Example: Shipping Company Support

To demonstrate the power of this approach, I’ve built a prototype for a shipping company’s customer support system. Here’s a simplified dialogue from the prototype:

User: “I need to change the delivery address for my package”

AI: “I’d be happy to help you update the delivery address for your package. First, I’ll need your tracking number.”

[RENDERS FORM WITH TRACKING NUMBER FIELD]

User: [Submits form with tracking number “TRK12345678”]

AI: “Thank you! I’ve found your package to [original address]. It’s currently in transit and scheduled for delivery tomorrow. Would you like to change the delivery address?”

[RENDERS YES/NO BUTTONS]

User: [Clicks “Yes” button]

AI: “Please provide the new delivery address where you’d like your package delivered.”

[RENDERS ADDRESS FORM WITH MULTIPLE FIELDS]

User: [Completes form with new address information]

AI: “Thank you for providing your new address details. Here’s a summary of the changes:”

[RENDERS TABLE COMPARING OLD AND NEW ADDRESS]

[RENDERS CONFIRMATION BUTTONS]

User: [Clicks “Confirm” button]

AI: “Perfect! I’ve updated the delivery address for your package TRK12345678. The delivery date has been adjusted to May 18 due to the address change. You’ll receive an email confirmation shortly.”

[RENDERS PROGRESS INDICATOR SHOWING UPDATED DELIVERY TIMELINE]

This interaction demonstrates the fluid integration of conversational AI with purpose-built UI components. At each step, the appropriate interface element is generated based on the current context and user needs.

Implementation Approach

The implementation of AI-generated UIs requires several key components:

1. System Prompting

The LLM needs clear instructions about available components and when to use them. Here’s an excerpt from my system prompt:

You can generate interactive UI components as part of your responses.

When users need to:
- Provide multiple pieces of information → Generate a FORM
- Make a choice between options → Generate BUTTONS
- View structured data → Generate a TABLE or LIST
- See entity details → Generate a CARD

For each component type, include JSON in this format:

\`\`\`ui-component
{
  "type": "componentType",
  "data": { /* component-specific properties */ }
}
\`\`\`

Always provide both a conversational text response AND appropriate UI components.
Multiple components can be included when appropriate, each in its own code block.

Here are the components you can use:
...

2. Client-Side Rendering

The client application needs to:

• Extract UI component JSON from the LLM response
• Validate component structure
• Render the appropriate component with proper styling
• Handle user interactions with the component
• Send structured data back to the LLM

Here’s an example of how a raw response from the LLM might look for the shipping address change scenario, and how it would be parsed:

Thank you for providing your new address details. Here's a summary of the changes:

\`\`\`ui-component
{
  "type": "table",
  "data": {
    "columns": ["Detail", "Old Address", "New Address"],
    "rows": [
      {"Detail": "Street", "Old Address": "123 Main St", "New Address": "456 Oak Ave"},
      {"Detail": "City", "Old Address": "Los Angeles", "New Address": "San Francisco"},
      {"Detail": "Postal Code", "Old Address": "90001", "New Address": "94107"},
      {"Detail": "Country", "Old Address": "USA", "New Address": "USA"}
    ]
  }
}
\`\`\`

Do you confirm?

\`\`\`ui-component
{
  "type": "buttons",
  "data": [
    {"label": "Confirm Changes", "value": "confirm_address_change", "variant": "primary"},
    {"label": "Edit Address", "value": "edit_address", "variant": "secondary"},
    {"label": "Cancel", "value": "cancel_changes", "variant": "danger"}
  ]
}
\`\`\`

In practice, this response might be embedded within the streaming text using special markers. The client-side code would:

1. Extract the UI components: Parse the LLM response to identify any ui-component specifications
2. Process each component: Validate and transform the component data for rendering
3. Render the components: Use the appropriate React/Vue/etc. components to display the UI
4. Handle interactions: Set up event handlers for user interactions

Here’s simplified pseudocode for the client processing:

function processLLMResponse(rawResponse) {
  const response = convertRawResponseToJSON(rawResponse);

  // Extract the text content
  const textContent = response.text;
  
  // Display the text portion of the response
  displayMessage(textContent);
  
  // Process each UI component
  if (response.components && response.components.length > 0) {
    response.components.forEach(component => {
      // Validate component structure
      if (!validateComponent(component)) {
        console.error("Invalid component:", component);
        return;
      }
      
      // Render the appropriate component based on type
      switch (component.type) {
        case "table":
          renderTable(component.data);
          break;
        case "buttons":
          renderButtons(component.data, handleButtonClick);
          break;
        case "form":
          renderForm(component.data, handleFormSubmit);
          break;
        // Other component types...
      }
    });
  }
}

function handleButtonClick(buttonValue) {
  // Send the button value back to the LLM
  sendToLLM({
    type: "ui_interaction",
    component: "button",
    value: buttonValue
  });
}

This approach allows for both synchronous and streaming responses, with UI components appearing seamlessly alongside text content.

3. Design System

A consistent design system ensures components:

• Follow accessibility standards
• Adapt to different screen sizes
• Support dark/light modes
• Maintain consistent visual language
• Support internationalization

Challenges and Future Directions

While promising, AI-generated UIs face several challenges:

Technical Challenges

1. Latency: Generating UI specifications adds time to response generation
2. Validation: Ensuring generated component specifications are valid and secure
3. State management: Maintaining component state during conversation flow
4. Cross-platform rendering: Supporting consistent experiences across devices
5. Integration complexity: Ensuring compatibility across platforms (iOS, Android, web, etc.)

UX Challenges

1. Expectation management: Setting appropriate user expectations about AI capabilities
2. Interaction design: Creating natural transitions between text and UI interactions
3. Accessibility: Ensuring generated UIs are accessible to all users
4. Consistency: Maintaining visual and behavioral consistency across generated components
5. Discoverability: Helping users understand what interactions are possible

Future Research Directions

Looking ahead, several promising areas for further research include:

1. Automated UI testing: Systems that can test generated interfaces for usability and accessibility
2. Personalized interfaces: Adapting component generation to individual user preferences and needs
3. Multi-turn UI interactions: Components that evolve across multiple conversation turns
4. Predictive UI generation: Preemptively generating interfaces based on conversation trajectory
5. Visual-linguistic alignment: Better integration between visual and textual elements
6. Enhanced MCP integration: Standardized protocols for UI component generation across MCP services

Getting Started with Your Own Implementation

For developers interested in implementing AI-generated UIs in their own applications, I recommend starting with these steps:

1. Define your component library and specifications
2. Create a robust system prompt that instructs the LLM when and how to generate components
3. Implement client-side rendering for each component type
4. Add validation to ensure components meet structure and security requirements
5. Start with simple components (buttons, simple forms) before moving to more complex ones
6. Test extensively with real users to identify interaction pain points
7. Refine prompting strategy based on user testing results

Conclusion

The integration of AI-generated user interfaces with conversational systems represents a significant leap forward in human-computer interaction. By dynamically creating the right interface components at the right time, we can address the inherent limitations of pure text interaction while preserving the flexibility and naturalness of conversation.

From my work with MCPs and this UI generation prototype, I’ve found that the most powerful AI experiences arise not from pure text or pure visual interfaces, but from their thoughtful combination. The future of AI interaction isn’t about choosing between conversation and traditional UIs—it’s about letting AI intelligently generate the right interface for each moment in the user journey.

_{This article was proofread and edited with AI assistance.}