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Analytics

DebuggAI provides powerful analytics capabilities to help you understand your AI application's performance and behavior.

What is AI Analytics?​

AI analytics is the process of analyzing data from AI applications to gain insights into their performance, usage, and behavior. Unlike traditional analytics, AI analytics focuses on the unique aspects of AI systems, such as:

  • Prompt Effectiveness: How well your prompts are designed to elicit the desired responses
  • Model Performance: How quickly and efficiently your models are responding
  • Response Quality: How accurate, relevant, and useful the model's responses are
  • Cost Efficiency: How much you're spending on AI API calls and whether you're getting good value

Key Analytics Features​

DebuggAI offers several key analytics features:

Performance Analytics​

Analyze your AI application's performance:

  • Latency: Track how quickly your models are responding
  • Token Usage: Track how many tokens are being used for prompts and responses
  • Throughput: Track how many requests your application is handling
  • Error Rates: Track how often your requests are failing

Cost Analytics​

Analyze the cost of your AI API calls:

  • Total Cost: Track the total cost of your AI API calls
  • Cost by Model: Track the cost of each model you're using
  • Cost by Feature: Track the cost of each feature in your application
  • Cost Trends: Track how your costs are changing over time

Usage Analytics​

Analyze how your AI application is being used:

  • Request Volume: Track the number of requests over time
  • Active Users: Track how many users are using your AI features
  • Feature Adoption: Track which AI features are being used the most
  • User Behavior: Track how users are interacting with your AI features

Quality Analytics​

Analyze the quality of your AI application's outputs:

  • Response Quality: Track the quality of model responses
  • Hallucination Rate: Track how often your models are generating false or misleading information
  • User Satisfaction: Track how satisfied users are with your AI features
  • Improvement Opportunities: Identify opportunities to improve your AI features

Analytics Dashboards​

DebuggAI provides several pre-built dashboards to help you analyze your AI application:

  • Overview Dashboard: A high-level overview of your AI application's performance
  • Performance Dashboard: Detailed performance metrics for your AI application
  • Cost Dashboard: Detailed cost metrics for your AI application
  • Usage Dashboard: Detailed usage metrics for your AI application
  • Quality Dashboard: Detailed quality metrics for your AI application

You can also create custom dashboards to focus on the metrics that matter most to your team.

Best Practices​

Here are some best practices for analyzing your AI applications:

  • Define Key Metrics: Define the key metrics that matter most to your team
  • Set Baselines: Set baselines for your metrics to track improvements
  • Track Trends: Track how your metrics are changing over time
  • Segment Data: Segment your data to understand different user groups
  • Act on Insights: Use your insights to make improvements to your AI application

Next Steps​

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

  • Explore Error Tracing to learn more about tracking requests in detail
  • Learn about Jump to Error to see how DebuggAI gets your right to the root of the problem