Overview

This observability framework enables AI teams to trace model behaviour, detect anomalies, and apply evaluation metrics to improve AI accuracy, reliability, and efficiency.

This section covers:

• Concepts: Foundational knowledge about observability, its importance in LLM performance tracking, and how it helps diagnose issues like hallucinations, bias, and inefficiencies.
• How-To Guide: Step-by-step instructions for leveraging observability tools, including:
  • Creating an Observe project to begin monitoring model behaviour.
  • Importing and analysing LLM traces to track model outputs over time.
  • Applying evaluations to measure accuracy, coherence, and relevance.
  • Filtering and debugging AI responses to isolate problematic outputs.
  • Setting up alerts for real-time anomaly detection.
  • Comparing model performance across different versions and datasets.

By mastering observability, you can ensure AI reliability, diagnose model weaknesses, and make data-driven decisions to improve LLM performance.