Conversation Patterns

Real conversations are rarely linear—users switch topics, correct themselves, and ask follow-up questions. Conversation patterns are reusable system flows that are provided by CALM and enable your AI assistant to handle these non-linear interactions cooperatively, and repair the conversation when customers don’t follow the path you expect them to.

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Use conversation patterns for smarter AI assistants

You can’t predict everything a user will say—and with conversation patterns, you don’t have to. Conversation patterns make your assistant more flexible and effective by:

• Keeping conversations on track: Handle unexpected inputs seamlessly, leading to more successful outcomes and a better user experience.
• Simplifying design and development: Helps the team focus on crafting great user journeys and business logic instead of accounting for every possible detour.

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How Conversation Patterns work

Here’s an overview of the different types of conversation patterns:

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Conversation Patterns in Action

Here is an example of a conversation pattern in each category listed above:

1. Correction (repair pattern): Update and correct previously stored conversational information.

User: My name is Jonathan, not John. correction triggered Bot: Thanks for letting me know! It’s important to get things right. Should I update your name to Jonathan in my records? User: Yes, please. updated first name to Jonathan Bot: Great! I’ve updated your name to Jonathan. Let me know if there’s anything else I can assist with. Example showcasing corrections

2. Cancellation (navigation pattern): Allows a user to stop a task in the middle of one topic or task.

User: I want to send some money to Dimitri Bot: How much money do you want to send? User: Ah, never mind. I see I have already sent it earlier. cancellation triggered Bot: Okay, I am canceling the transfer. Example showcasing error handling

3. Start a conversation (navigation pattern): Conversations can be initiated by either your assistant or the user.

session started Bot: Hi there! How can I assist you today? Example showcasing a simple assistant greeting

4. Search (external support pattern): Enables your assistant to query data from your knowledge base with Rasa’s enterprise search feature and provide helpful answers.

User: What is your policy on returns? pattern search started Bot: We accept returns within 30 days of purchase if they are returned in new condition and original packaging. Would you like to start a return process? Example showcasing enterprise search

5. Repeat (voice pattern): Enables your assistant to repeat a previous message.

Bot: Your order #1377 is on it’s way. Would you like to track it? User: Can you repeat that? repeat triggered Bot: Of course, your order #1377 is on its way. Would you like to track it? Example showcasing a user asking the assistant to repeat the last message

6. Errors (system pattern): Invalid responses or technical issues might require apologies or alternative actions. In these scenarios, you might need to re-ask questions, confirm intent, or escalate to a human agent.

User: Update my order details. internal error: request timed out Bot: Oops! It looks like something went wrong while processing your request. Let me try that again or escalate this issue. Example showcasing error handling

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Customizing Conversation Patterns

Rasa provides a default structure for each pattern, but you are free to customize them to fit your use case. Since patterns are system flows, you can edit them like any other flow in Rasa Pro or Rasa Studio. We recommend keeping the contextual response rephraser enabled for pattern flows as it helps your assistant sound more natural, especially when repairing dynamic interactions.

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Learn more about Patterns

• Read the complete pattern reference documentation
• Use Rasa Studio to edit conversation patterns