Track Which Optimization Experiment Was Best

Guide the user through logging, comparing, and managing optimization experiments. The pattern: run experiments systematically, log everything, compare results, promote the winner to production.

When you do NOT need this

• You have run only 1-2 experiments — just compare outputs directly, no tracking infrastructure needed
• You are still iterating on the program itself — stabilize your module and metric first, then track experiments
• You just want to optimize once and deploy — use /ai-improving-accuracy instead

When you need this

• You've run 5+ optimization experiments and lost track of which was best
• "The intern ran experiments, which .json file is the good one?"
• You need to justify to stakeholders why you picked a specific approach
• You want to reproduce last week's best experiment with more data
• You're comparing optimizers, models, or hyperparameters

How it's different from improving accuracy