Phoenix is primarily used for AI observability, experimentation, evaluation, and troubleshooting. It helps AI engineers and data scientists visualize data, evaluate model performance, track down issues, and export data for improvements. Key use cases include monitoring LLM applications, evaluating model performance, identifying and addressing model drift, and optimizing retrieval systems.Would you like more detail on any specific use case?Sources:
- Phoenix User Guide
- Phoenix: AI Observability & Evaluation
💡Hint: Mention RunLLM in the thread for followups.
