Getting Started with LLM Monitoring
Fiddler LLM Monitoring Introduction
What is LLM Monitoring?
Large Language Models (LLMs) are powerful but introduce unique challenges around accuracy, safety, and reliability. Effective monitoring is critical for detecting issues like hallucinations, toxic content, and performance degradation in production LLM applications.
How Fiddler LLM Monitoring Works
Fiddler's LLM monitoring solution tracks your AI application's inputs and outputs, then enriches this data with specialized metrics that measure quality, safety, and performance. These enrichments provide visibility into how your LLM applications behave in production, enabling you to:
Detect problematic responses before they impact users
Identify patterns of failure across your applications
Track performance trends over time
Analyze root causes when issues occur
Key Capabilities
Comprehensive Metrics: Monitor hallucinations, toxicity, relevance, latency, and many other LLM-specific metrics
Real-time Analysis: Track performance as it happens with intuitive dashboards
Advanced Enrichments: Generate embeddings, similarity scores, and specialized trust metrics automatically
Drift Detection: Identify when prompts or responses drift from expected patterns
RAG-specific Monitoring: For retrieval-augmented applications, analyze retrieval quality and source relevance
Getting Started
Implementing Fiddler LLM monitoring requires just three steps:
Onboard your LLM application to Fiddler by defining its inputs, outputs, and which enrichment metrics you need
Publish your application data to Fiddler, including prompts, responses, and context
Monitor performance through dashboards and alerts that track the metrics most important to your use case
Fiddler automatically handles the complex work of generating enrichments, detecting anomalies, and providing the visualizations you need to maintain high-quality LLM applications.
Next Steps
Learn:
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