The practical decision tree

Why this matters

Everything in this module collapses into one repeatable decision tree you can run in five minutes before any LLM project: is it knowledge or behavior, do you have data and an eval, have you exhausted prompting, and is a small fine-tuned model the cheapest path to your latency or cost goal. Encoding the whole module as a single function makes the decision fast, consistent, and shareable with your team. This is the artifact you'll actually reuse — the rest of the course assumes you've run it and landed on 'yes, fine-tune.'

Demo

The demo composes the whole module into one decision tree that returns a concrete recommendation, so you never start a fine-tune on instinct again.

Try it yourself

1. Run decide() on three real projects and record what it recommends for each.
2. Reorder two checks and reason about whether the order changes any outcome (it should be intentional).
3. Add a branch for 'classification with a fixed label set' and decide where it lands.
4. Save this function as your team's pre-fine-tune gate and write one sentence on why each branch exists.

References

• Hugging Face — PEFT documentation
• OpenAI — Fine-tuning guidance