Fine-Tune Readiness

1 · Purpose

Fine-tuning is the last resort in a ladder of cheaper levers, this skill makes the team climb the ladder in order, and gates the dataset before a single training run.

2 · When to use

• Whenever "we should fine-tune" is proposed.
• Before any SFT/LoRA/DPO run, to gate the dataset.
• When a fine-tuned model underperforms and nobody knows why (usually: the data).

3 · Inputs

• The task spec with metric and gate (spec-first-ai-feature).
• An eval that can score the behavior change (eval-first-development).
• The failure log from the current prompt/RAG system, clustered (error-analysis-50).

4 · Procedure

1. Climb the ladder in order, recording the score each rung achieves: (a) better prompting → (b) few-shot examples → (c) RAG with the right documents → (d) fine-tuning. Quote the score at each rung; do not skip rungs.
2. Name the failure class fine-tuning is meant to fix. Fine-tuning fixes behavioral classes, format, tone, style, domain vocabulary, consistent refusal/compliance patterns. It does not fix missing facts (that is RAG) or weak reasoning (that is a bigger model or decomposition).
3. If the failure class is knowledge or freshness, STOP: use RAG. If it is format or tone and rungs a-c are exhausted, proceed.
4. Gate the dataset before training:
   • ≥ 200 examples for SFT format/behavior shifts (1,000+ for real moves); preference pairs for DPO.
   • Every example reviewed or generated against a written standard.
   • No PII unless the compliance sign-off exists in writing.
   • A held-out slice (≥ 10%) that training never sees.
5. Choose the method: LoRA/QLoRA first (small adapter, reversible, cheap); full fine-tune only when adapters demonstrably cannot move the metric. DPO only after SFT, when you have preference pairs.
6. Compute the hardware budget (local-model-fit) or the cloud training cost before launching. Write the number down.
7. Train, then score on the held-out slice and the golden set. Compare against the best prompt/RAG rung, not against the untrained base model alone.
8. Ship only if the fine-tune beats the best non-trained rung by a margin that justifies the serving and maintenance cost. Record the decision and the numbers.

5 · Anti-rationalization

Excuse	Answer
"Fine-tuning is the professional way."	The professional way is the cheapest rung that clears the gate. Often that is a prompt.
"More data will fix it."	200 reviewed examples beat 5,000 scraped ones. Data quality is the gate, not volume.
"It learned the format, so it works."	Format on training data is memorization. The held-out slice is the exam.
"Everyone fine-tunes now; it's cheap."	Training is cheap; owning a model's drift, evals, and retraining forever is not.

6 · Red flags

• The proposal names a method (LoRA, DPO) before naming the failure class.
• The dataset was never read by a human.
• Success is defined against the base model, not against the best prompted system.
• No one can say what happens when the policy changes and the model must be retrained.

7 · Verify

• The ladder scores are recorded for prompting, few-shot, and RAG rungs.
• The dataset passed the four gates (size, standard, PII, held-out slice).
• The fine-tuned model's golden-set score and the best non-trained score are both written down, and the ship/keep-prompting decision cites them.

8 · ZoroLogistics example

Week 10: the ops team wants replies in the company voice with strict section order. Prompting gets 0.74 on the style rubric; few-shot 0.81; RAG adds nothing (the class is behavioral, so the ladder says fine-tune). 600 SFT examples written against the style standard, LoRA on a 14B at QLoRA, held-out slice 60. Fine-tuned: 0.93 vs. few-shot 0.81, margin justifies the adapter. DPO follows with 300 preference pairs for tone, lifting helpfulness without breaking format. Decision: ship the adapter, keep the prompts under version control as the fallback.

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