Curates, deduplicates, and formats training datasets for fine-tuning so that the resulting model actually improves on target behaviors rather than learning noise. Fine-tuning preparation covers dataset quality filtering, output format consistency, train/test splits, and avoiding common pitfalls like data leakage that invalidate fine-tuning results.
Use cases
- Preparing a domain-specific fine-tuning dataset for a model that will handle medical, legal, or technical terminology
- Adapting a general-purpose model to a specific company's writing style or response format
- Fine-tuning to improve a specific capability (code completion, summarization, translation) where base model performance is insufficient
- Creating a fine-tuning dataset from internal conversation logs or support tickets to build a specialized assistant
- When base model evaluation reveals a specific failure mode that fine-tuning should address
Key features
- Gather raw examples relevant to the target behavior and deduplicate them—near-duplicate examples bias the model toward over-represented patterns
- Filter for quality and correctness: remove examples where the desired output is wrong, ambiguous, or of low quality even if the input is valid
- Format examples consistently as instruction-response pairs or chat templates depending on the target fine-tuning approach (SFT, RLHF, DPO)
- Split into train and evaluation sets, ensuring no data leakage—evaluation examples must be from the same distribution but not overlap with training examples
- Document the dataset composition, quality criteria, and known limitations so the fine-tuning run is reproducible and the results are interpretable
When to Use This Skill
- When a general-purpose model has a consistent failure mode on your specific task that prompt engineering cannot fix
- When you have enough domain-specific examples (hundreds to thousands) to demonstrate the desired behavior
- When you need consistent output format or style that general instruction following cannot reliably produce
Expected Output
A clean, deduplicated, formatted fine-tuning dataset with train/eval splits, quality criteria documentation, and a reproducibility record.
Frequently Asked Questions
- How many examples do I need for fine-tuning?
- It depends on the task and model size. Simple style or format tasks may need 100-500 examples. Complex domain knowledge tasks typically need 1,000-10,000. Start with the minimum viable dataset and evaluate whether more data improves results before scaling up.
- What is the most common fine-tuning mistake?
- Data leakage—allowing evaluation examples in the training set. This inflates evaluation metrics and produces a model that appears to work well in testing but fails on real data. Maintain strict separation between train and eval sets.
- Should I fine-tune or use RAG for a knowledge-intensive task?
- RAG is generally preferred for knowledge-intensive tasks because it is more transparent (you can audit the retrieved documents) and easier to update (swap documents, not retrain). Fine-tune when RAG adds too much latency, when you need offline capability, or when the task requires a specific reasoning pattern rather than factual recall.
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