Project: Fine-Tune Your Own Model
Fine-tuning is not the first solution for every problem. It is useful when you need a model to behave differently, not when you just need new facts.
Use RAG for facts. Use fine-tuning for form, behavior, style, tool patterns, or narrow task skill.
Project decision
Fine-tune only if you can answer yes:
| Question | Why it matters |
|---|---|
| Do we have evals? | otherwise improvement is guesswork |
| Do we have high-quality examples? | bad data trains bad behavior |
| Is prompting insufficient? | fine-tuning is expensive |
| Is RAG the wrong tool? | facts belong outside weights |
| Can we deploy and monitor it? | training is only half the work |
Good fine-tuning use cases
- consistent JSON/tool-call format
- company support tone
- domain-specific classification
- narrow extraction task
- small model matching a larger model on routine cases
- lower latency or lower cost through specialization
Dataset design
Each example should include:
- input
- ideal output
- reason it is correct
- edge case label
- source or reviewer
- quality status
Avoid mixing random examples. A fine-tune should have a clear target behavior.
Training approach
For most teams, start with PEFT:
| Method | When |
|---|---|
| LoRA | standard adapter-based fine-tuning |
| QLoRA | lower-memory training |
| full fine-tune | only when you have the budget and need |
| distillation | when a smaller model should imitate a stronger one |
Evals before and after
Run the same eval set against:
- baseline prompt
- stronger reference model
- fine-tuned model
- cheaper smaller model if relevant
Measure:
- task accuracy
- format validity
- refusal correctness
- latency
- cost
- failure cases
Deployment plan
Do not switch all traffic instantly.
offline eval -> internal test -> 5 percent canary -> compare traces -> gradual rollout
Rollback if quality drops, schema failures rise, or cost/latency misses the target.
Knowledge check
Q1: What should you use for new factual knowledge?
Usually RAG or tools, not fine-tuning.
Q2: Why do evals come before training?
They define what improvement means and prevent training by vibes.