How do you design an LLM experiment tracking system?

Experiment tracking systems log prompts, models, parameters, outputs, and evaluation metrics for reproducibility and comparison.

LLM experiments are highly variable, so tracking must include full context: prompt version, model version, temperature, retrieval configuration, and evaluation scores. The system enables side-by-side comparison of experiments and supports rollback to better-performing configurations. It functions similarly to MLflow but extended for LLM-specific artifacts.