CORE GUIDE
Fine-tuning dataset design
Fine-tuning dataset design turns a measured behavior gap into representative training examples with clear labels, coverage and provenance while keeping evaluation cases separate enough to tell whether training actually generalized.
Mental model
Training data is a specification expressed through examples. Its distribution tells the model which behavior to reinforce, so dataset design should mirror the target gap without leaking the test that will later judge success.
Why it matters
Adapters and fine-tuning make optimization easy to start, but weak data can encode noise, shortcuts or historical mistakes. A smaller curated dataset that represents the failure modes often teaches more than a large dump of production logs and produces evidence that is easier to audit.
01
Design from the target failure distribution
Define the behavior to change and collect examples that cover common, difficult and boundary cases. Normalize labels and instructions, remove duplicates and leakage, track source and consent constraints, and split by meaningful units such as user, document or time when random row splits would leak near-duplicates. Hold out slices that represent the original failure so training and evaluation remain independent.
02
Example: support fine-tune memorizes repeated ticket templates
A team trains on thousands of historical tickets and reports near-perfect validation accuracy using a random split. Many tickets are near-duplicate templates, so the same patterns appear in train and test. Splitting by customer and time, deduplicating templates and curating hard escalation cases reveals a much smaller real gain and a clearer target for the adapter.
Common failure modes
- Using raw production logs as training data without curation or provenance.
- Randomly splitting near-duplicate examples and mistaking leakage for generalization.
- Optimizing the dataset for average frequency while omitting costly boundary cases.
Engineering heuristics
- Start from a measured behavior gap and design examples to cover it.
- Split data on the unit that would create real-world leakage if shared.
- Keep held-out failure slices outside the training loop until release evaluation.
Takeaways
- 01Fine-tuning data is an executable behavior specification.
- 02Curation and split design determine whether evaluation is believable.
- 03More examples do not compensate for leakage or missing failure coverage.
Reading evidence
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Used in
This Concept is reused across these canonical learning paths.