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Debugging

DebuggAI provides powerful debugging tools to help you identify and fix issues in your AI applications.

What is AI Debugging?​

AI debugging is the process of identifying and fixing issues in AI applications. Unlike traditional debugging, AI debugging focuses on the unique aspects of AI systems, such as:

  • Prompt Engineering: Identifying issues with how prompts are constructed
  • Model Behavior: Understanding why a model responded in a certain way
  • Response Quality: Evaluating the quality of model responses
  • Performance Issues: Identifying bottlenecks and inefficiencies

Key Debugging Features​

DebuggAI offers several key debugging features:

Request Tracing​

Trace requests through your application to understand how they're processed:

  • Prompt History: See the full history of prompts sent to the model
  • Response History: See the full history of responses received from the model
  • Metadata: View additional information about each request
  • Performance Metrics: See how long each request took and how many tokens were used

Prompt Analysis​

Analyze your prompts to identify issues and opportunities for improvement:

  • Prompt Quality: Evaluate the quality of your prompts
  • Prompt Suggestions: Get suggestions for improving your prompts
  • Prompt Comparison: Compare different versions of a prompt to see which performs better
  • Prompt Templates: Create and manage prompt templates to ensure consistency

Response Evaluation​

Evaluate the quality of model responses:

  • Response Quality: Evaluate the quality of responses based on various criteria
  • Hallucination Detection: Identify when a model is generating false or misleading information
  • Response Comparison: Compare different responses to the same prompt
  • Response Templates: Create and manage response templates to ensure consistency

Error Analysis​

Analyze errors to understand why they occurred:

  • Error Types: Categorize errors by type
  • Error Patterns: Identify patterns in errors
  • Error Resolution: Get suggestions for resolving errors
  • Error Tracking: Track errors over time to see if they're being resolved

Debugging Workflow​

Here's a typical debugging workflow with DebuggAI:

  1. Identify the Issue: Use monitoring to identify issues in your AI application
  2. Trace the Request: Use request tracing to understand how the request was processed
  3. Analyze the Prompt: Use prompt analysis to identify issues with the prompt
  4. Evaluate the Response: Use response evaluation to identify issues with the response
  5. Fix the Issue: Make changes to your application based on your findings
  6. Verify the Fix: Monitor the application to ensure the issue is resolved

Best Practices​

Here are some best practices for debugging your AI applications:

  • Add Context: Add metadata to your requests to provide context for debugging
  • Use Tracing: Use request tracing to understand how requests are processed
  • Compare Versions: Compare different versions of prompts and responses to see which performs better
  • Test Systematically: Test changes systematically to ensure they have the desired effect
  • Document Findings: Document your findings to help others understand the issues and solutions

Next Steps​

Now that you understand how debugging works in DebuggAI, you can:

  • Learn about Analytics to see how to analyze your AI application's performance and behavior
  • Explore Request Tracing to learn more about tracking requests in detail