Experiment Tracking.prompt

# Experiment Tracking for LLM Development

## Overview

Experiment tracking captures and organizes the results of LLM experiments — prompt variations, model comparisons, hyperparameter sweeps, and evaluation results. Systematic tracking enables reproducibility, informed decision-making, and knowledge sharing across teams. Without it, teams waste time re-running experiments or making decisions based on incomplete information.

## Key Concepts

- **Experiment logging** — Recording parameters, metrics, and artifacts for each run
- **Prompt versioning** — Tracking prompt text as a first-class experiment parameter
- **Model comparison** — Structured comparison of outputs across different models
- **Ablation studies** — Systematically varying one parameter to understand its impact
- **Cost tracking** — Recording token usage and API costs per experiment
- **Reproducibility** — Capturing enough state to re-run experiments identically

## Implementation Patterns