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LLM Fine-tuning

Adapt base models to niche domains with clean datasets and evals.

Why it matters

Fine-tuning narrows a model's behavior to your domain and tone.

Core steps

  • Curate high-signal examples; remove contradictions and duplicates.
  • Split data into train/validation/test with stable seeds.
  • Track metrics like loss, win-rate, and exact match against eval sets.
  • Compare against a strong baseline before rolling out.

Checklist

  • [ ] Balanced classes
  • [ ] PII scrubbed
  • [ ] Clear stop sequences
  • [ ] Regression evals saved