12.05 · Concept
Instruction Tuning Datasets
Design an instruction tuning dataset with prompts, responses, task categories, quality labels, and train-validation-test splits.
Instruction tuning data is a governed collection of prompt and response records that teaches a foundation model to follow user requests. Good design covers task types, response standards, quality labels, provenance, and clean train, validation and test splits so evaluation measures generalisation rather than memorised examples.
What this lesson answers
- how to structure instruction tuning dataset records
- what quality labels instruction tuning data needs
- how to split instruction tuning data safely
Notes
Instruction tuning datasets teach a foundation model how to follow human instructions rather than simply predict the next token from raw text. For a working data engineer, the key idea is to treat each training example as a structured record: an instruction or prompt, any optional context, the expected response, a task category, and metadata about quality. The prompt should clearly represent what a user would ask, and the response should demonstrate the behavior you want the model to learn: accurate, complete, safe, and in the desired style.
Common questions
- What fields should an instruction tuning dataset include?
- Each record should include the user instruction, any needed context, the target response, a task category, and quality metadata. Useful operational fields include source, version, language, reviewer status, policy constraints and provenance. Treat the dataset as a schema-managed data product, not a loose pile of prompt and response text.
- Why add task categories to instruction tuning examples?
- Task categories make coverage visible. They let you check whether the data reflects the behaviours the model must learn, such as summarisation, extraction, classification, question answering, code work, rewriting or domain workflows. They also help compare model quality across capabilities instead of relying on one blended score.
- How should train, validation and test splits be made?
- Splits should prevent leakage between sets while preserving important task types and difficulty levels. Keep near-duplicates, related source documents and repeated workflows in the same split where possible. Use training data for learning, validation data for development choices, and reserve the test set for final measurement.
Short definition: what is Instruction Tuning Datasets?