Fine-Tuning
// Fine-tune LLMs with data preparation, provider selection, cost estimation, evaluation, and compliance checks.
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updated:March 4, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
nameFine-Tuning
slugfine-tuning
descriptionFine-tune LLMs with data preparation, provider selection, cost estimation, evaluation, and compliance checks.
When to Use
User wants to fine-tune a language model, evaluate if fine-tuning is worth it, or debug training issues.
Quick Reference
| Topic | File |
|---|---|
| Provider comparison & pricing | providers.md |
| Data preparation & validation | data-prep.md |
| Training configuration | training.md |
| Evaluation & debugging | evaluation.md |
| Cost estimation & ROI | costs.md |
| Compliance & security | compliance.md |
Core Capabilities
- Decide fit — Analyze if fine-tuning beats prompting for the use case
- Prepare data — Convert raw data to JSONL, deduplicate, validate format
- Select provider — Compare OpenAI, Anthropic (Bedrock), Google, open source based on constraints
- Estimate costs — Calculate training cost, inference savings, break-even point
- Configure training — Set hyperparameters (learning rate, epochs, LoRA rank)
- Run evaluation — Compare fine-tuned vs base model on task-specific metrics
- Debug failures — Diagnose loss curves, overfitting, catastrophic forgetting
- Handle compliance — Scan for PII, configure on-premise training, generate audit logs
Decision Checklist
Before recommending fine-tuning, ask:
- What's the failure mode with prompting? (format, style, knowledge, cost)
- How many training examples available? (minimum 50-100)
- Expected inference volume? (affects ROI calculation)
- Privacy constraints? (determines provider options)
- Budget for training + ongoing inference?
Fine-Tune vs Prompt Decision
| Signal | Recommendation |
|---|---|
| Format/style inconsistency | Fine-tune ✓ |
| Missing domain knowledge | RAG first, then fine-tune if needed |
| High inference volume (>100K/mo) | Fine-tune for cost savings |
| Requirements change frequently | Stick with prompting |
| <50 quality examples | Prompting + few-shot |
Critical Rules
- Data quality > quantity — 100 great examples beat 1000 noisy ones
- LoRA first — Never jump to full fine-tuning; LoRA is 10-100x cheaper
- Hold out eval set — Always 80/10/10 split; never peek at test data
- Same precision — Train and serve at identical precision (4-bit, 16-bit)
- Baseline first — Run eval on base model before training to measure actual improvement
- Expect iteration — First attempt rarely optimal; plan for 2-3 cycles
Common Pitfalls
| Mistake | Fix |
|---|---|
| Training on inconsistent data | Manual review of 100+ samples before training |
| Learning rate too high | Start with 2e-4 for SFT, 5e-6 for RLHF |
| Expecting new knowledge | Fine-tuning adjusts behavior, not knowledge — use RAG |
| No baseline comparison | Always test base model on same eval set |
| Ignoring forgetting | Mix 20% general data to preserve capabilities |