PebbleObserve - AI Usage

Observability Overview

Observability in PebbleAI helps you understand what’s happening inside your AI applications. It provides visibility into AI behavior, performance metrics, and helps you debug issues quickly.

What is AI Observability?

AI observability goes beyond traditional monitoring by providing:

  • Trace Analysis: See exactly how your AI processes requests
  • Performance Metrics: Understand response times and costs
  • Quality Monitoring: Track output quality and user satisfaction
  • Debugging Tools: Quickly identify and fix issues

Core Observability Features

📊 Real-time Monitoring

Watch your AI applications as they run:

  • Live request tracking
  • Response time analysis
  • Token usage monitoring
  • Error rate tracking

🔍 Detailed Tracing

Every AI interaction is automatically traced:

  • Input/output logging
  • Model selection tracking
  • Prompt template usage
  • Chain of thought visualization

📈 Analytics Dashboard

Comprehensive insights at a glance:

  • Usage trends over time
  • Cost analysis by model
  • Performance benchmarks
  • User interaction patterns

🎯 Quality Metrics

Monitor and improve AI output quality:

  • Response accuracy tracking
  • User feedback integration
  • A/B testing results
  • Prompt effectiveness scores

Key Components

Traces

A trace represents a complete AI interaction:

User Input → Prompt Processing → Model Call → Response Generation → Output

Each step is logged with:

  • Timestamps
  • Token counts
  • Model parameters
  • Intermediate outputs

Sessions

Group related traces into sessions:

  • Track multi-turn conversations
  • Analyze user journeys
  • Identify conversation patterns
  • Measure session success rates

Metrics

Key performance indicators:

  • Latency: Response time measurements
  • Cost: Token usage and pricing
  • Volume: Request counts and trends
  • Quality: Accuracy and satisfaction scores

Logs

Detailed event logging:

  • System events
  • Error messages
  • Debug information
  • Custom events

Observability Workflow

1. Automatic Instrumentation

When you use PebbleAI:

  • All API calls are automatically traced
  • No code changes required
  • Zero-overhead implementation
  • Works with all models

2. Real-time Visibility

Access your observability dashboard to see:

  • Current active requests
  • Recent traces
  • System health
  • Alert status

3. Deep Dive Analysis

Click on any trace to:

  • View complete request details
  • Analyze token usage
  • Review prompt templates
  • Examine model responses

4. Continuous Improvement

Use insights to:

  • Optimize prompts
  • Reduce costs
  • Improve response quality
  • Fix performance issues

Integration with Visual Builder

Observability works seamlessly with the Visual AI Builder:

Automatic Flow Tracking

  • Every node execution is traced
  • See data flow between components
  • Identify bottlenecks
  • Monitor parallel executions

Visual Debugging

  • Click nodes to see their traces
  • Color-coded performance indicators
  • Real-time execution status
  • Error highlighting

Performance Optimization

  • Identify slow nodes
  • Optimize token usage
  • Test different configurations
  • Compare flow versions

Common Use Cases

Debugging Failed Requests

  1. Find the Error

    • Search for failed traces
    • Filter by error type
    • View stack traces

  2. Analyze Context

    • Check input data
    • Review prompt templates
    • Examine model parameters

  3. Fix and Verify

    • Update configuration
    • Test with replay
    • Monitor improvements

Optimizing Costs

  1. Identify High-Cost Operations

    • Sort by token usage
    • Group by model type
    • Analyze usage patterns

  2. Optimize Usage

    • Switch to efficient models
    • Reduce prompt sizes
    • Cache common responses

  3. Track Savings

    • Compare before/after
    • Monitor cost trends
    • Set budget alerts

Improving Response Quality

  1. Collect Feedback

    • User satisfaction scores
    • Manual quality reviews
    • Automated evaluations

  2. Analyze Patterns

    • Identify poor responses
    • Find common issues
    • Compare prompt versions

  3. Iterate and Improve

    • Update prompts
    • Fine-tune parameters
    • A/B test changes

Best Practices

Effective Tracing

  • Use Session IDs: Group related requests
  • Add Metadata: Include user context
  • Set Sampling: Balance detail vs. volume
  • Monitor Costs: Set up usage alerts

Dashboard Organization

  • Create Views: Customize for your needs
  • Set Filters: Focus on important data
  • Build Alerts: Proactive monitoring
  • Share Insights: Team collaboration

Performance Optimization

  • Baseline First: Establish normal metrics
  • Test Changes: Measure improvements
  • Monitor Continuously: Catch regressions
  • Document Findings: Build knowledge base

Advanced Features

Custom Metrics

Define your own measurements:

pebbleai.track('custom_metric', {
  value: responseQuality,
  metadata: { department: 'support' }
})

Alerts and Notifications

Set up proactive monitoring:

  • Error rate thresholds
  • Cost limit warnings
  • Performance degradation
  • Custom conditions

Export and Integration

Get data where you need it:

  • Export to CSV/JSON
  • Webhook notifications
  • API access
  • Third-party integrations

Langfuse Integration

We’re working on deep integration with Langfuse for enhanced observability:

  • Advanced trace analysis
  • Prompt management
  • Evaluation pipelines
  • Team collaboration features

Stay tuned for updates!

Getting Started

  1. Enable Observability

    • Automatic for all PebbleAI users
    • No configuration required

  2. Access Dashboard

    • Navigate to Observability tab
    • View your first traces
    • Explore metrics

  3. Start Optimizing

    • Identify improvement areas
    • Make changes
    • Measure impact

Next Steps

PebbleAgent - Visual AI BuilderWorkspaces & Collaboration