Using Inputs on Topics in Copilot Studio
Introduction
- New feature in Copilot Studio: inputs on topics
- Similar concept to inputs and outputs on actions
- Eliminates the need for question nodes in custom topics
- Enables using an LLM for entity extraction and updating previous inputs seamlessly
- Example: Ordering a pizza and changing the details mid-conversation
Benefits of Inputs on Topics
- Simplifies creation of powerful custom topics with minimal effort
- Supports hierarchical entities in slot filling
- Contextual conversation flow with dynamic entity extraction
- Streamlined user experiences
Demonstration Overview
- Scenario: Scheduling a consultation with a business expert
- Old Approach: Series of question nodes for collecting inputs
- New Approach: Using inputs and outputs directly within topics
Practical Example
- Environment: Inside Copilot Studio's new UX
- Example Topic: Requesting a device repair
- Trigger Phrase: "Request device repair"
- Inputs: Model number and serial number
- Input Mechanism: Explanation
- Provides variables without question nodes
- Allows to change inputs dynamically in the conversation
Features of Inputs
- Inputs and outputs can be defined within topic details
- Includes variables like Surface model and serial number
- Ability to create new variables as needed
- Similar to action inputs
- Includes entity validation and other question node capabilities
Outputs
- Outputs can also be defined with descriptions and examples
- Enhances bot's interaction effectiveness
Conclusion
- Inputs on topics are transformative for building bots in Copilot Studio
- Encourages dynamic, context-aware conversation design
- Practical impact: Streamlining bot development and enhancing user experiences
Call to Action: Try Copilot Studio at aka.ms TR copilot Studio
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