Use Case: An AI-powered chatbot generates a misleading response—the trace log helps pinpoint the issue and diagnose why it occurred. 2. Session-Based Observability LLM applications often involve multi-turn interactions, making it essential to group related traces into sessions.
Use Case: A virtual assistant handling customer queries must track response relevance over multiple turns to ensure coherent assistance. 3. Automated Evaluation & Scoring Observe provides structured evaluation criteria to score AI performance based on predefined metrics.
Use Case: A content generation model produces AI-written summaries. Observe automatically scores the summary’s accuracy, coherence, and relevance. 4. Historical Trend Analysis Observability is not just about real-time monitoring—it also involves tracking model behaviour over time.
Use Case: A team updates its legal AI assistant—historical data shows whether the new version improves or worsens accuracy. 5. Automated Issue Detection & Alerts To ensure AI systems remain functional, Observe enables automated issue detection and alerting.
Use Case: A customer service AI model starts generating unexpected responses—Observe triggers an alert, allowing the team to investigate immediately.By providing a comprehensive observability framework, Observe empowers AI teams to build more reliable, fair, and high-performing LLM applications in production environments.