Logs
The Logs section in Waterflai's Analytics provides detailed information about individual AI model calls and interactions. This powerful tool allows you to dive deep into the specifics of your AI applications' operations.
Accessing the Logs
Navigate to the Analytics section in your Waterflai workspace.
Select the "Logs" tab from the tabbed interface.
Using the Logs Interface
Date Range Selection
Use the Date Range Picker at the top of the Analytics page to select the time period for which you want to view logs. The logs view will update to show entries within the selected range.
Log Entries
Each log entry typically includes:
Timestamp: When the AI model call or interaction occurred.
User ID: Identifier for the user who initiated the interaction (if applicable).
Model: The specific AI model used for the interaction.
Input: A snippet or summary of the input provided to the AI model.
Output: A snippet or summary of the AI model's response.
Response Time: How long the AI model took to generate a response.
Status: Whether the interaction was successful or if there was an error.
Filtering and Searching
The Logs interface likely includes options to filter and search log entries. Common filter options might include:
Status (e.g., successful, error)
Model type
Response time range
Use the search functionality to look for specific terms in inputs or outputs.
Interpreting Log Data
Look for patterns in errors or unusually long response times.
Identify frequently used inputs or types of queries.
Monitor the usage of different AI models within your applications.
Track specific user interactions if troubleshooting user-reported issues.
Use Cases for Logs
Troubleshooting: Investigate specific errors or issues reported by users.
Performance Optimization: Identify slow-performing queries or models.
Usage Analysis: Understand how users are interacting with your AI applications in detail.
Quality Assurance: Review AI model outputs for accuracy and appropriateness.
Compliance: Maintain records of AI interactions for regulatory or internal compliance needs.
Best Practices
Regularly review logs to stay aware of your AI applications' operations.
Use log data in conjunction with dashboard metrics for a complete picture of performance.
Set up alerts for critical errors or performance thresholds.
Ensure that your log retention policies comply with relevant data protection regulations.
Use insights from logs to inform model selection, prompt engineering, and application design.
Privacy and Security Considerations
Ensure that sensitive information is properly masked or encrypted in log entries.
Restrict access to log data to authorized personnel only.
Be aware of data retention laws and regulations that may apply to your log data.
By effectively using the Logs feature, you can gain deep insights into your AI applications' operations, troubleshoot issues quickly, and continuously improve your AI-driven services.
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