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:- 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.
- Ensuring Model Reliability Detect unexpected hallucinations, misinformation, or irrelevant outputs. Identify task completion failures and ambiguous AI responses.
- Improving Model Accuracy & Alignment Apply predefined evaluation templates to measure coherence, accuracy, and response quality. Automate scoring based on performance benchmarks and structured criteria.
- 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.
- Monitoring Bias & Fairness Evaluate AI responses for ethical risks, safety concerns, and compliance adherence. Apply bias-detection metrics to maintain responsible AI behaviour.
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.