Lesson deck
Fine-tuning data, risks and shipping flashcards
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- Front
- A tuned model looks fine offline but worse in production. First suspect?
- Back
- A prompt-format mismatch: training used a different system prompt or chat template than production. Train on the exact serving format.
- Front
- Name four risks of fine-tuning.
- Back
- Forgetting (general abilities regress), overfitting (memorises training examples), safety regression (weaker safety behaviour, even with benign data) and leakage (training data overlaps your evals).
- Front
- What do you compare a fine-tuned model against before shipping?
- Back
- The base model with the best prompt, plus RAG if facts matter. If the tune does not clearly beat that baseline, stay with the baseline.
- Front
- What do you need in place so a bad adapter can be reversed quickly?
- Back
- Adapters versioned together with their data and hyperparameters, a canary release, and a rollback path to the previous adapter or the base model.
- Front
- What is decontamination and why does it matter?
- Back
- Removing training examples that overlap your eval data. If they overlap, the scores measure memorisation and mean little.