As LLMs transition from experimentation to production, ensuring their reliability, fairness, and efficiency becomes critical. The Observe feature is designed to provide AI teams with real-time insights, evaluation metrics, and diagnostic tools to monitor and improve LLM-based applications.

This feature goes beyond simple monitoring, it enables teams to trace model behaviour, detect anomalies, measure AI performance, and diagnose issues such as hallucinations, inconsistencies, and inefficiencies.

By leveraging automated scoring, structured evaluation criteria, and historical trend analysis, Observe helps AI teams fine-tune LLM performance, debug failures, and optimize models for long-term reliability.

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Features of Observe

The Observe feature is built with five core objectives that help AI teams track, diagnose, and optimize LLM behaviour in production environments:

  1. Real-Time Monitoring Track LLM-generated responses, system telemetry, and model behaviour in live applications. Visualise AI operations with structured trace logs and session analysis. ​
  2. Ensuring Model Reliability Detect unexpected hallucinations, misinformation, or irrelevant outputs. Identify task completion failures and ambiguous AI responses. ​
  3. Improving Model Accuracy & Alignment Apply predefined evaluation templates to measure coherence, accuracy, and response quality. Automate scoring based on performance benchmarks and structured criteria. ​
  4. Accelerating Debugging & Problem-Solving Pinpoint issues by analysing traces, sessions, and response deviations. Use structured logs and failure patterns to diagnose and fix model inefficiencies. ​
  5. Monitoring Bias & Fairness Evaluate AI responses for ethical risks, safety concerns, and compliance adherence. Apply bias-detection metrics to maintain responsible AI behaviour. ​

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Core Components of Observe

1. LLM Tracing & Debugging

Observability starts with LLM Tracing, which captures every input-output interaction, system response, and processing time in an LLM-based application.

  • Trace Identification – Assigns a unique trace ID to every AI response for tracking and debugging.
  • Response Auditing – Logs input queries, AI-generated responses, and execution times.
  • Error Detection – Highlights failed completions, latency issues, and incomplete outputs.

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.

  • Session IDs – Cluster multiple interactions within a single conversation or task execution.
  • Conversation Analysis – Evaluate how AI performs across a sequence of exchanges.
  • Performance Trends – Track how AI evolves within a session, ensuring consistency.

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.

  • Evaluation Templates – Predefined models for coherence, completeness, and user satisfaction.
  • Scoring System – Uses quantitative metrics to assess response effectiveness.
  • Pass/Fail Flags – Automatically detect responses that fall below a quality threshold.
  • Real-Time Evaluations – Apply automated scoring to AI-generated responses as they occur.
  • Custom Criteria – Define organization-specific evaluation metrics to tailor observability to unique use cases.

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.

  • Performance Trends – Compare past vs. present AI behaviour to measure improvement.
  • Cross-Model Comparisons – Analyze different versions of an LLM to assess enhancements.
  • Statistical Insights – Apply standard deviation, percentiles, and response distributions to detect long-term anomalies.

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.

  • Live Monitoring – Observe token consumption, processing delays, and response failures in real time.
  • Threshold-Based Alerts – Notify users if error rates or latency exceed safe limits.
  • Workflow Automation – Automatically flag and log problematic interactions for further analysis.

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.

OverviewComponents of Observability