OpenDialog Docs
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  • GETTING STARTED
    • Introduction
    • Getting ready
    • Billing and plans
    • Quick Start AI Agents
      • Quick Start AI Agent
      • The "Start from Scratch" AI Agent
        • Chat Management Conversation
        • Welcome Conversation
        • Topic Conversation
        • Global No Match Conversation
        • Supporting LLM Actions
        • Semantic Classifier: Query Classifier
      • A Process Handling AI Agent
  • STEP BY STEP GUIDES
    • AI Agent Creation Overview
    • Add a new topic of discussion
    • Use knowledge sources via RAG
    • Adding a structured conversation
    • Add a 3rd party integration
    • Test and tweak your AI Agent
    • Publish your AI Agent
  • CORE CONCEPTS
    • OpenDialog Approach
      • Designing Conversational AI Agents
    • OpenDialog Platform
      • Scenarios
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      • Language Services
      • OpenDialog Account Management
        • Creating and managing users
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    • OpenDialog Conversation Engine
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      • Contexts
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  • CREATE AI APPLICATIONS
    • Designing your application
      • Conversation Design
        • Conversational Patterns
          • Introduction to conversational patterns
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          • Openings
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            • Using in OpenDialog
          • Information collection
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            • Using in OpenDialog
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            • Example dialog
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            • Using in OpenDialog
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        • Best practices
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          • Conversation structure
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          • NLU modeling
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          • The team
            • What does a conversation designer do
          • Select resources
      • Message Design
        • Message editor
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        • Subsequent Messages - Virtual Intents
        • Using Attributes in Messages
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        • Message Types
          • Text Message
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          • Date Picker Message
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          • List Message
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          • Meta Messages
            • Progress Bar Message
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      • Webchat Interface design
        • Webchat Interface Settings
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      • Accessibility
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    • Leveraging Generative AI
      • Language Services
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          • OpenAI
          • Azure
          • Google Gemini
          • Output attributes
        • Retrieval Augmented Generation
        • Example-based intent classification [Deprecated]
      • Interpreters
        • Available interpreters
          • OpenDialog interpreter
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            • Google Dialogflow interpreter
            • Google Dialogflow Knowledge Base
          • OpenAI interpreter
        • Using a language service interpreter
        • Interpreter Orchestration
        • Troubleshooting interpreters
      • LLM Actions
        • OpenAI
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        • Output attributes
        • Using conversation history (memory) in LLM actions
        • LLM Action Analytics
    • 3rd party Integrations in your application
      • Webhook actions
      • Actions from library
        • Freshdesk Action
        • Send to Email Action
        • Set Attributes Action
      • Conversation Hand-off
        • Chatwoot
    • Previewing your application
    • Launching your application
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    • Debugging your application
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    • FAQ
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  • Developing With OpenDialog
    • Integrating with OpenDialog
    • Actions
      • Webhook actions
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    • WebChat
      • Chat API
      • WebChat authentication
      • User Tracking
      • Load Webchat within page Element
      • How to enable JavaScript in your browser
      • SDK
        • Methods
        • Events
        • Custom Components
    • External APIs
  • Release Notes
    • Version 3 Upgrade Guide
    • Release Notes
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On this page
  1. CREATE AI APPLICATIONS
  2. Designing your application
  3. Conversation Design
  4. Conversational Patterns

Information collection

Information is always being collected during a conversation, with every question, story, or any other exchange. But sometimes specific information is needed to accomplish the goal of the interaction. The information collection process could be system-driven, where the bot leads the user through specific areas of interest. It could also be user-driven, where the user provides an extended account. With user-driven information collection, the system must be capable of processing and handling the diversity of incoming information appropriately.

In addition to conversationally collected information, it is also possible to use external sources like a user profile or an API pass-through to collect information. In the components below, we discuss some common approaches to information collection with conversational bots.

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Last updated 7 months ago