phoenix-observability

You can trace LLM calls by instrumenting your application with OpenTelemetry to visualize execution flows. This helps debug inconsistent chatbot responses by tracking prompts, responses, and intermediate steps to pinpoint logic deviations.

LLM observability tracks production AI systems in real-time to detect performance degradation and errors. You need it to ensure reliability and monitor execution flows when running large language model applications in production.

Yes, you can run systematic evaluations on datasets to measure model performance, hallucination rates, and relevance. This evaluation framework helps pinpoint the root cause of errors by testing datasets against expected outcomes.

Yes, this observability approach requires OpenTelemetry instrumentation to trace LLM applications. It supports various LLM frameworks, allowing you to monitor execution flows and evaluate model performance systematically.

The best way to monitor production AI systems is using an observability platform that tracks real-time insights. By instrumenting with OpenTelemetry, you can monitor performance degradation, evaluate datasets, and trace execution flows.

LLM application logic can deviate due to inconsistent prompts or intermediate steps. By using LLM tracing to visualize the execution flow, you can identify exactly where the logic deviates and run evaluations on test datasets to fix it.