adaptation-strategies
Installation
SKILL.md
Adaptation Strategies — Fine-tune vs ICL vs RAG vs Distillation
You have a base model. It doesn't do the task well enough. You have four levers to change that, and they are not interchangeable — each changes a different thing. Picking the wrong one is the most expensive recoverable mistake in applied AI: weeks of labeling, a training run, and an eval suite, all to solve a problem a 200-token prompt would have fixed.
The whole discipline reduces to one question: what kind of gap do you have?
| Gap | Symptom | Right lever |
|---|---|---|
| Knowledge | Model doesn't know a fact, doc, or current state | RAG |
| Behavior / format | Model knows enough but won't reliably do it the way you need (tone, structure, JSON, domain register) | Fine-tune (after prompt+RAG exhausted) |
| Capability | Model literally can't — reasoning depth, hard task it fails at any prompt | Bigger model, then ICL to steer it |
| Cost / latency at scale | Quality is fine, the bill or the p95 isn't | Distill or fine-tune a small model |
Memorize that table. Everything below is detail on each lever and the failure modes of using one in the wrong row.