Learning roadmap
Fine-Tuning & Adaptation
Know when to reach for fine-tuning (rarely), do it right when you must, and run the before/after evals that prove the new model is actually better — then keep it that way in production.
4 stages~8 h
That leaves 4 stages, ~8 hours of focused work. You can clear it in any order, but the stages compound — try them top-to-bottom your first time.
- You'll pick up:
style vs facts, decision tree, rag first rule, task definition, do nothing option
Practice quizzesLab taskBuild a "decision tree" page: a case-intake form (what changes? what data? what quality gap?) that walks a branching diagram and lands on a verdict with the reason and the cheaper alternative first.
Build it in the LabCheckpointThree real cases decided with reasons — and at least one was saved from an unnecessary fine-tune.
What you'll be able to do
- Decide prompt vs RAG vs fine-tune with a stated reason — and defend "do nothing" when it wins.
- Build an instruction dataset that passes its own quality gates: deduped, licensed, balanced.
- Run a LoRA fine-tune whose before/after eval numbers decide whether it ships.
- Operate the fine-tuned model: versioned, monitored, with a re-tune trigger and a cost line.
Or try a related path