Response Formats: Markdown vs. Interactive UI

Chat Data supports two primary ways for your chatbot to respond to users: Text-based (Markdown) and Interactive Visual Responses (UI). Understanding when to use each allows you to create the most engaging experience.

Text-based Responses (Markdown)

This is the default mode for most chatbots. Responses are generated as text, supporting standard Markdown formatting.

Features

• Rich Text: Bold, italics, lists, and headers.
• Links: Clickable hyperlinks.
• Code Blocks: Formatted code snippets.
• Simplicity: Works universally across all platforms and integrations.

Best for: General conversation, Q&A, explanations, and simple text-based guidance.

Interactive Visual Responses (UI)

Launched December 2025

This mode allows your chatbot to render rich, interactive graphical components directly in the chat interface.

Features

• Forms: Input fields, dropdowns, and checkboxes for structured data collection.
• Cards & Carousels: Visually appealing ways to display products, articles, or profiles.
• Action Buttons: One-click actions to trigger workflows or open links.
• Real-time Data: Display dynamic tables, charts (if supported), and status dashboards.

Best for: E-commerce, lead generation, booking appointments, and complex data presentation.

Platform Support

• Website Widget: Full support for all interactive components.
• Messaging Apps (WhatsApp, Slack, etc.): Automatically falls back to text-based representations to ensure functionality.

How to Configure

You can control the response format globally or per workflow.

Global Configuration

To set the default behavior for your entire chatbot:

1. Go to Settings > Model Configuration (or AI Settings).
2. Locate the Response Format option.
3. Select Text-based or Interactive.

Specific Workflows

You can mix and match formats within a workflow:

• Use standard LLM blocks for text responses.
• Use the Dynamic UI Composer block specifically when you need to render a visual component.

This hybrid approach gives you the flexibility to "show" when necessary and "tell" when appropriate.