phoenix-observability

LLM tracing visualizes the entire lifecycle of LLM calls, including prompts and intermediate steps, using OpenTelemetry. This allows you to identify exactly where context was misinterpreted and debug the issue within your application.

Systematic evaluation on LLM datasets involves running built-in evaluators or LLM-as-judge metrics to assess model performance. This process helps identify regressions and ensures new prompt versions fix issues before deployment.

Yes, you can monitor production AI systems in real-time by integrating with OpenTelemetry for trace collection. This enables continuous performance tracking and allows you to compare different prompts or models to optimize system behavior.

No specific dependencies are required to use this observability platform. You can install the necessary libraries directly via pip and launch the server to begin tracing, evaluating, and monitoring your LLM applications.

You can evaluate and compare different prompts by running systematic evaluations on test datasets and tracking production systems. This experimentation identifies which prompt versions yield the best performance and resolve issues.