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
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        • Semantic Classifier: Query Classifier
      • A Process Handling AI Agent
  • STEP BY STEP GUIDES
    • AI Agent Creation Overview
    • Add a new topic of discussion
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    • Adding a structured conversation
    • Add a 3rd party integration
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  • 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
          • Building robust assistants
            • Contextual help
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            • Contextual FAQ
          • Openings
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          • Authentication
            • Components
            • Example dialog
            • Using in OpenDialog
          • Information collection
            • Components
            • Example dialog
            • Using in OpenDialog
            • Additional information
          • Recommendations
            • Components
            • Example dialog
            • Additional information
          • Extended telling
            • Components
            • Example dialog
            • Additional information
          • Repair
            • Types of repair
            • User request not understood
            • Example dialog
            • Additional information
          • Transfer
            • Components
            • Example dialog
            • Additional information
          • Closing
            • Components
            • Example dialog
            • Using in OpenDialog
            • Additional information
        • Best practices
          • Use Case
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            • Primary research
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            • Outcome: user profile
          • Assistant personality
          • Sample dialogs
          • Conversation structure
          • API Integration Capabilities
          • NLU modeling
          • Testing strategy
          • The team
            • What does a conversation designer do
          • Select resources
      • Message Design
        • Message editor
        • Constructing Messages
        • Message Conditions
        • Messages best practices
        • Subsequent Messages - Virtual Intents
        • Using Attributes in Messages
        • Using Markdown in messages
        • Message Types
          • Text Message
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          • Button Message
          • Date Picker Message
          • Audio Message
          • Form Message
          • Full Page Message
          • Conversation Handover message
          • Autocomplete Message
          • Address Autocomplete Message
          • List Message
          • Rich Message
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          • E-Sign Message
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          • Meta Messages
            • Progress Bar Message
          • Attribute Message
      • Webchat Interface design
        • Webchat Interface Settings
        • Webchat Controls
      • Accessibility
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    • Leveraging Generative AI
      • Language Services
        • Semantic Intent Classifier
          • OpenAI
          • Azure
          • Google Gemini
          • Output attributes
        • Retrieval Augmented Generation
        • Example-based intent classification [Deprecated]
      • Interpreters
        • Available interpreters
          • OpenDialog interpreter
          • Amazon Lex interpreter
          • Google Dialogflow
            • Google Dialogflow interpreter
            • Google Dialogflow Knowledge Base
          • OpenAI interpreter
        • Using a language service interpreter
        • Interpreter Orchestration
        • Troubleshooting interpreters
      • LLM Actions
        • OpenAI
        • Azure OpenAI
        • 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
    • Monitoring your application
    • Debugging your application
    • Translating your application
    • FAQ
    • Troubleshooting and Common Problems
  • Developing With OpenDialog
    • Integrating with OpenDialog
    • Actions
      • Webhook actions
      • LLM actions
    • 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
  5. Recommendations

Example dialog

In a Hospitality use case, a hotel guest may want a restaurant recommendation.

Bot: I can recommend local restaurants for you. What kind of restaurant are you looking for?

User: Maybe an Italian restaurant nearby, not too expensive.

Bot: It sounds like you’re looking for an Italian restaurant, within 5 miles, that is inexpensive. Is that right?

User: Yes.

Bot: Great. One option is Bella Italia at 520 Main St., about a 5 minute walk from here. It gets good ratings and isn’t too expensive. How does that sound?

User: Seems good.

Bot: If interested, I can book a reservation for you, or call them for you.

User: Let’s make a reservation for tonight at 7pm.

In this example, the system recommends the restaurant Bella Italia to the user. Before doing so, however, the system collected the relevant user preferences to be able to make such a recommendation. This process is documented in more detail in the Collecting Information pattern section, filling slots for things like type of food, location and cost. Once those slots are filled, the system has enough information to recommend a restaurant.

In this sample, the user accepts the system’s recommendation, and the conversation moves on to making a reservation. If the user had declined the reservation, the system could make another recommendation or ask for more user preferences. This could include asking for why that recommendation wasn’t a fit, e.g. it was too expensive. With this extra information, the system can refine its recommendations.

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Last updated 1 year ago