Instruct Dataset Generation Pipeline
Turn a raw document corpus into a clean, leak-free, well-covered instruction-tuning dataset through seven clear stages.
Description
A seven-stage pipeline that turns a messy pile of documents into a clean dataset for teaching a model to follow instructions, ready for supervised fine-tuning. The stages are: pull out candidate examples, remove duplicates, remove anything that overlaps your test set, filter for quality, check coverage, add data where it is thin, then package it. The pipeline rests on a clear finding. For a task-specific fine-tune, 100 to 100,000 carefully prepared examples beat a much larger but dirty pile. Quality, not quantity, is the lever.
When to apply
Use this when preparing the instruction-tuning dataset for a supervised fine-tune of an LLM, such as a domain model, an LLM twin, or a task-specific assistant. It helps most when the starting material is messy, such as scraped pages, exported chats, or mixed-quality archives. Do not use it when you already have a hand-curated instruction set of proven quality. Two exceptions: skip the add-data stage when the corpus is large and evenly spread, and skip the overlap-removal stage only when the test set was made after the fact and shares nothing public, which is rare.
What it involves
- Extract candidate instruction-response pairs
- Deduplicate
- Decontaminate against the evaluation set
- Quality-filter
- Explore for coverage
- Augment under-represented dimensions
- Package and version
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