Simulate Conversations
Overview
The ElevenLabs Conversational AI API allows you to simulate and evaluate text-based conversations with your AI agent. This guide will teach you how to implement an end-to-end simulation testing workflow using the simulate conversation endpoints (batch and streaming), enabling you to granularly test and improve your agent’s performance to ensure it meets your interaction goals.
Prerequisites
- An agent configured in ElevenLabs Conversational AI (create one here)
- Your ElevenLabs API key, which you can create in the dashboard
Implementing a Simulation Testing Workflow
Identify initial evaluation parameters
Search through your agent’s conversation history and find instances where your agent has underperformed. Use those conversations to create various prompts for a simulated user who will interact with your agent. Additionally, define any extra evaluation criteria not already specified in your agent configuration to test outcomes you may want for a specific simulated user.
Simulate the conversation via the SDK
Create a request to the simulation endpoint using the ElevenLabs SDK.
This is a basic example. For a comprehensive list of input parameters, please refer to the API reference for Simulate conversation and Stream simulate conversation endpoints.
Analyze the response
The SDK provides a comprehensive JSON object that includes the entire conversation transcript and detailed analysis.
Simulated Conversation: Captures each interaction turn between the simulated user and the agent, detailing messages and tool usage.
Analysis: Offers insights into evaluation criteria outcomes, data collection metrics, and a summary of the conversation transcript.
Improve your evaluation criteria
Review the simulated conversations thoroughly to assess the effectiveness of your evaluation criteria. Identify any gaps or areas where the criteria may fall short in evaluating the agent’s performance. Refine and adjust the evaluation criteria accordingly to ensure they align with your desired outcomes and accurately measure the agent’s capabilities.
Improve your agent
Once you are confident in the accuracy of your evaluation criteria, use the learnings from simulated conversations to enhance your agent’s capabilities. Consider refining the system prompt to better guide the agent’s responses, ensuring they align with your objectives and user expectations. Additionally, explore other features or configurations that could be optimized, such as adjusting the agent’s tone, improving its ability to handle specific queries, or integrating additional data sources to enrich its responses. By systematically applying these learnings, you can create a more robust and effective conversational agent that delivers a superior user experience.
Continuous iteration
After completing an initial testing and improvement cycle, establishing a comprehensive testing suite can be a great way to cover a broad range of possible scenarios. This suite can explore multiple simulated conversations using varied simulated user prompts and starting conditions. By continuously iterating and refining your approach, you can ensure your agent remains effective and responsive to evolving user needs.
Pro Tips
Detailed Prompts and Criteria
Crafting detailed and verbose simulated user prompts and evaluation criteria can enhance the effectiveness of the simulation tests. The more context and specificity you provide, the better the agent can understand and respond to complex interactions.
Mock Tool Configurations
Utilize mock tool configurations to test the decision-making process of your agent. This allows you to observe how the agent decides to make tool calls and react to different tool call results. For more details, check out the tool_mock_config input parameter from the API reference.
Partial Conversation History
Use partial conversation histories to evaluate how agents handle interactions from a specific point. This is particularly useful for assessing the agent’s ability to manage conversations where the user has already set up a question in a specific way, or if there have been certain tool calls that have succeeded or failed. For more details, check out the partial_conversation_history input parameter from the API reference.