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AI Analytics

NopeSight's AI Analytics provides intelligent insights into your reporting system usage, optimizes query performance, and helps improve the overall user experience through automated analysis and recommendations.

What AI Analytics Provides​

Query Performance Analysis​

  • Response Time Monitoring: Track how quickly AI queries are processed
  • Success Rate Tracking: Monitor query success and failure rates
  • Popular Query Patterns: Identify most commonly used queries
  • Optimization Recommendations: Suggestions for improving query performance

User Behavior Insights​

  • Query Frequency: Most requested report types and data
  • User Satisfaction: Feedback and rating analysis
  • Usage Patterns: When and how users interact with reports
  • Feature Adoption: Which AI features are most valuable

System Optimization​

  • Prompt Template Performance: A/B testing of AI prompts
  • Auto-Optimization: Continuous improvement of AI responses
  • Resource Usage: Monitor AI service costs and efficiency
  • Error Pattern Analysis: Identify and fix common issues

Available Features​

1. Query Analytics Dashboard​

Access comprehensive analytics about AI query usage:

  • Total Queries: Number of AI queries processed
  • Success Rate: Percentage of successful queries
  • Average Response Time: Performance metrics
  • Popular Query Types: Most common report requests
  • User Satisfaction Scores: Feedback ratings and comments

2. User Feedback System​

Collect and analyze user feedback:

  • Rating System: 1-5 star ratings for AI responses
  • Helpful/Not Helpful: Quick feedback buttons
  • Detailed Comments: Text feedback for improvements
  • Selected Results: Track which results users find most useful

3. Query Pattern Analysis​

Understand how users interact with AI:

  • Common Patterns: Frequently used query structures
  • Trending Topics: Popular data requests over time
  • User Preferences: Individual and team usage patterns
  • Seasonal Variations: Usage changes over time

4. Prompt Template Optimization​

Improve AI response quality:

  • A/B Testing: Test different prompt variations
  • Performance Metrics: Track template effectiveness
  • Auto-Optimization: Continuous improvement based on feedback
  • Version Control: Track changes and improvements

How to Use AI Analytics​

1. Access the Dashboard​

  1. Navigate to Reports → AI Analytics
  2. View overall system performance metrics
  3. Analyze query trends and patterns
  4. Review user feedback and satisfaction scores

2. Provide Feedback​

When using AI-generated reports:

  1. Use the star rating system (1-5 stars)
  2. Click Helpful or Not Helpful buttons
  3. Add detailed comments for improvements
  4. Select the most useful results

3. Monitor Performance​

Track system performance over time:

  • Response Times: How quickly AI responds to queries
  • Success Rates: Percentage of successful queries
  • Error Patterns: Common issues and failures
  • Resource Usage: Cost and efficiency metrics

4. Review Recommendations​

Check optimization suggestions:

  • Prompt Improvements: Better ways to phrase queries
  • Performance Optimizations: Faster query methods
  • Feature Suggestions: New capabilities to request
  • Training Opportunities: Areas for user education

Available API Endpoints​

Feedback Submission​

POST /api/ai-analytics/feedback
{
  "queryId": "query_id",
  "rating": 5,
  "helpful": true,
  "comment": "Great analysis!",
  "selectedResults": ["result1", "result2"]
}

Dashboard Data​

GET /api/ai-analytics/dashboard?timeRange=30d

Query Patterns​

GET /api/ai-analytics/query-patterns
GET /api/ai-analytics/popular-queries?limit=10

Template Performance​

GET /api/ai-analytics/templates

Analytics Dashboard​

Key Metrics​

  • Total AI Queries: Number of queries processed
  • Success Rate: Percentage of successful responses
  • Average Response Time: Query processing speed
  • User Satisfaction: Average rating from feedback
  • Popular Query Types: Most requested report types
  • Response Time Over Time: Performance improvements
  • Query Volume: Usage growth patterns
  • Error Rates: System reliability metrics
  • User Engagement: Adoption and usage trends

User Insights​

  • Most Active Users: Power users and adoption champions
  • Popular Templates: Most used report templates
  • Feedback Trends: User satisfaction over time
  • Feature Requests: Common improvement requests

Optimization Features​

1. Prompt Template A/B Testing​

Test different AI prompt variations:

  • Create Variants: Test different prompt phrasings
  • Performance Comparison: Compare response quality
  • Automatic Selection: Choose best performing prompts
  • Continuous Improvement: Ongoing optimization

2. Query Optimization​

Improve query performance:

  • Pattern Recognition: Identify common query structures
  • Caching Strategies: Cache frequently used results
  • Index Optimization: Improve database performance
  • Resource Allocation: Optimize AI service usage

3. User Experience Enhancement​

Improve the overall user experience:

  • Response Quality: Better, more accurate answers
  • Faster Processing: Reduced query response times
  • Error Reduction: Fewer failed queries
  • Personalization: Tailored responses based on usage

Best Practices​

1. Providing Feedback​

  • Be Specific: Detailed comments help improve the system
  • Rate Consistently: Use the full 1-5 star range
  • Explain Issues: Describe what went wrong or could be better
  • Highlight Successes: Note when AI performs well

2. Query Optimization​

  • Clear Requests: Be specific about what you want
  • Use Examples: Provide context for better results
  • Iterate and Refine: Improve queries based on results
  • Learn from Patterns: Use popular query structures

3. System Monitoring​

  • Regular Reviews: Check analytics regularly
  • Performance Tracking: Monitor response times
  • Error Analysis: Investigate common failures
  • User Training: Share best practices with team

Current Implementation​

What's Working​

  • Feedback Collection: User ratings and comments
  • Query Logging: Comprehensive query tracking
  • Pattern Analysis: Identification of common queries
  • Performance Monitoring: Response time and success tracking
  • Template Optimization: A/B testing framework

Optimization Process​

  1. Data Collection: Gather user feedback and performance metrics
  2. Pattern Analysis: Identify trends and common issues
  3. Optimization: Improve prompts and system performance
  4. Testing: Validate improvements through A/B testing
  5. Deployment: Roll out optimizations to all users

Troubleshooting​

Common Issues​

  • Slow Response Times: Check system load and optimize queries
  • Low Success Rates: Review query patterns and error logs
  • Poor User Ratings: Analyze feedback for improvement areas
  • Missing Data: Ensure proper logging and data collection

Performance Tips​

  • Monitor Regularly: Check analytics dashboard frequently
  • Act on Feedback: Use user comments to guide improvements
  • Test Changes: Validate optimizations before full deployment
  • Share Insights: Communicate findings with development team

Future Enhancements​

Planned Features​

  • Advanced Analytics: More detailed performance metrics
  • Predictive Analytics: Forecast usage patterns and needs
  • Automated Optimization: Self-improving AI responses
  • Integration Analytics: Cross-system usage insights

Improvement Areas​

  • Real-time Monitoring: Live performance dashboards
  • Custom Metrics: User-defined success criteria
  • Export Analytics: Download analytics reports
  • API Enhancements: More detailed programmatic access

Getting Help​

  • Documentation: Comprehensive guides and examples
  • Support: Contact your system administrator
  • Community: User forums and knowledge base
  • Training: AI optimization best practices