Comet Opik Tracing
The Comet Opik Tracing policy integrates Comet Opik with the Zuplo AI Gateway, enabling comprehensive observability, tracing, and evaluation of your LLM applications in both development and production environments.
Comet Opik is an open-source platform designed to help developers track, view, and evaluate Large Language Model (LLM) traces throughout the application lifecycle. By integrating Opik with the Zuplo AI Gateway, you gain complete visibility into your AI operations, from development debugging to production monitoring.
Key Capabilities
The Comet Opik integration provides powerful observability and evaluation features:
- Comprehensive trace logging — Automatically capture LLM calls, inputs, outputs, and metadata
- Development debugging — Annotate and label traces through SDK or UI for iterative improvement
- LLM evaluation — Use LLM-as-a-Judge and heuristic evaluators to score trace quality
- Production monitoring — Track feedback scores, trace counts, tokens, and performance metrics at scale
- High-volume ingestion — Support for up to 40 million traces per day
- Dataset management — Store and run evaluations on test datasets
Benefits with Zuplo AI Gateway
Integrating Comet Opik with the Zuplo AI Gateway provides several advantages:
Complete Application Observability
Track entire LLM workflows including preprocessing, retrieval steps, model calls, and post-processing through your API gateway, providing end-to-end visibility.
Development and Production Parity
Use the same tracing infrastructure in both development and production environments, ensuring consistent observability throughout your application lifecycle.
Automatic Trace Capture
The policy automatically logs all AI Gateway requests and responses without requiring code changes to your LLM application, simplifying instrumentation.
Performance Insights
Monitor token usage, latency, error rates, and costs across all your AI operations with detailed analytics dashboards.
Quality Assurance
Evaluate LLM outputs using both automated metrics and LLM-as-a-Judge approaches to maintain quality standards as your application evolves.
How It Works
Trace Logging
The policy captures comprehensive information about each LLM interaction:
- Request data — User prompts, input parameters, and metadata
- Response data — Model outputs, token counts, and generation details
- Performance metrics — Latency, processing time, and resource usage
- Custom metadata — Tags, conversation IDs, and application-specific data
Trace Organization
Traces are organized hierarchically to represent complex workflows:
- Traces — Top-level records representing complete user interactions
- Spans — Nested operations within a trace (retrieval, generation, etc.)
- Thread IDs — Group related traces by conversation or session
Evaluation Framework
Opik provides multiple evaluation approaches:
Heuristic Metrics
Deterministic evaluation methods including:
- Exact match — Verify outputs match expected values
- Contains — Check for presence of specific content
- Regex patterns — Validate output structure and format
LLM-as-a-Judge Metrics
AI-powered evaluation for subjective quality assessment:
- Hallucination detection — Identify factually incorrect outputs
- Relevance scoring — Measure response appropriateness
- Tone and style — Evaluate alignment with brand guidelines
- Safety checks — Detect harmful or inappropriate content
Use Cases
Debugging LLM Applications
Identify and fix issues in LLM applications by examining detailed trace logs, including inputs, outputs, and intermediate steps.
A/B Testing AI Models
Compare performance across different models, prompts, or configurations by analyzing traces grouped by experiment variants.
Cost Optimization
Monitor token usage patterns to identify optimization opportunities and reduce AI operation costs.
Compliance and Auditing
Maintain detailed audit logs of all AI interactions for regulatory compliance and security requirements.
Quality Regression Testing
Track LLM output quality over time using automated evaluations, catching degradation before it impacts users.
Conversation Analytics
Analyze multi-turn conversations using thread IDs to understand user journeys and improve conversational AI experiences.
Additional Resources