name: experiment-design description: Use before changing a model, loss, optimizer, dataset pipeline, augmentation, or evaluation protocol for deep learning research work

Experiment Design

Turn a rough research idea into a falsifiable experiment plan before writing code.

Hard Gate

Do not change code, configs, or launch runs until these are clear:

• hypothesis
• baseline
• success metric
• dataset and split
• compute budget

Workflow

1. Inspect the current baseline: code path, configs, last known results, open uncertainties.
2. Ask clarifying questions one at a time.
3. State the hypothesis in one sentence: "If we change X, metric Y should improve because Z."
4. Propose 2-3 approaches and recommend one.
5. Define the first experiment as the smallest test that could disprove the idea.
6. Write an experiment card to docs/experiments/specs/YYYY-MM-DD-<topic>.md.
7. Get user approval before moving to experiment-planning.

Experiment Card

Every design doc should include:

• goal
• baseline to beat
• exact metric and selection rule
• train/val/test or benchmark split assumptions
• minimal code/config changes expected
• sanity checks before full training
• failure modes and what evidence would invalidate the idea
• next decision after the first run

Guardrails

• Do not hide evaluation changes inside modeling changes.
• Do not compare against a weak or mismatched baseline.
• Do not treat one lucky run as evidence.
• If the idea touches multiple subsystems, split it into separate experiments.