Training, Evaluation, and Deployment

Minimal training plan

Start with a small, controlled run before scaling:

1. Train split: 80%
2. Validation split: 10%
3. Test split: 10%
4. 3-5 hyperparameter variants max

Track both training and validation loss. If training drops while validation worsens, stop and regularize.

Evaluation gate before release

Pass all gates before deployment:

1. Task quality improves versus baseline.
2. No regression on core safety suite.
3. Structured output validity stays above target.
4. Cost and latency remain inside budget.

Rollout strategy

Use progressive exposure:

1. Internal dogfood.
2. 5% traffic shadow or canary.
3. 25% with active monitoring.
4. 100% after stability window.

Rollback readiness

Always keep:

• Previous model checkpoint ready.
• One-command rollback path.
• Alert thresholds for error spikes and refusal drift.

If (quality drops OR policy violations rise OR latency spikes) -> auto rollback

What great teams do differently

They treat finetuning as a product loop, not a one-time training event:

1. Collect failure data continuously.
2. Patch dataset monthly.
3. Re-run eval suites on every model update.
4. Ship improvements in small, measurable iterations.