Develop simulator dx

[luandro]

To move quick with development and identify potential edge cases, this issue focuses on building a simulator that uses AI to generate realistic conversational inputs, mimicking interactions from actual users. The Simulator will allow the system to run automated tests on the entire message processing flow, enabling continuous evaluation, improvement, and validation of functionalities before live deployment.

The simulator will serve as a foundational tool for accelerating development and improving system robustness, enabling rapid iteration on design and functionality by putting “machines to talk to each other.” By automating extensive testing, the simulator will help maintain high standards of accuracy, reliability, and user experience while identifying potential vulnerabilities and failure points.

Key Responsibilities

1. Real user simulation:

Create an AI-powered Simulator that generates varied, contextually relevant inputs across different conversation scenarios (e.g., greetings, transcription requests, research queries, grant writing interactions).

The Simulator should mimic real user behavior, providing both straightforward inputs and complex multi-step interactions to better represent real-world usage patterns.

2. Real world interaction flow simulation:

By simulating different types of user inputs, the system will evaluate the entire flow from message intake to response generation and ensure the correct routing and handling by the intent classifier and other plugins.

Implement evaluation frameworks (Langtrace) to assess the accuracy, response coherence, and plugin routing decisions for each simulated interaction.

3. Edge case simulation:

Use simulator to generate a diverse range of conversation flows, including incomplete inputs, ambiguous requests, and multi-layered queries, helping to identify edge cases where the system might falter or produce unexpected responses.

Develop logging and tracking tools to flag any system inconsistencies, misrouted messages, or failures, ensuring that edge cases can be addressed in development.

4. Guardrail simulation for malicious inputs:

The simulator should test the system’s guardrails by generating inputs that simulate malicious or inappropriate user behavior (e.g., offensive language, spam, security threats) to ensure that the system can detect, handle, and respond appropriately to these cases.

Evaluate and refine response strategies to maintain security, prevent exploitation, and enhance resilience against abuse.

5. Continuous evaluation and reporting:

Incorporate performance and accuracy metrics, generating reports after each simulation run to document flow accuracy, response quality, and any identified issues.

These evaluations will provide insights into strengths and areas for improvement, ultimately optimizing the user experience and system reliability.

Acceptance Criteria

• AI-based Simulator successfully generates diverse conversational inputs, representing realistic user interactions.

• Simulator evaluates and logs the intent classifier’s routing accuracy and response coherence for each test conversation.

• Edge cases are identified, logged, and tracked, allowing for targeted improvements and fine-tuning.

• Guardrails against malicious or inappropriate inputs are tested, and system responses are documented and refined.

• Post-simulation reports provide actionable insights on flow accuracy, edge cases, and guardrail efficacy.