Dataset Engineering
Dataset engineering refers to designing, collecting, curating, generating, annotating, optimizing the data needed for training and adapting AI models.
Imagine two problem types: classification and open chat response.
In closed-ended models, such as traditional classification models, dataset engineering is straightforward. For example, labeling an image as “cat” or “not a cat” is a well-defined task with clear ground truth.
However, in open-ended models, such as foundation models, dataset engineering becomes more complex. Since these models (through e.g. chatbot UI) can generate responses in an almost unlimited number of ways, it extends beyond simple labeling. Instead, dataset engineering focuses on tasks like deduplication, tokenization, context retrieval, quality control, removal of sensitive information.
How to prepare datasets, so specific model can train effectively on them?
Sources:
AI Engineering by Chip Huyen (O’Reilly). Copyright 2025 Developer Experience Advisory LLC, 978-1-098-16630-4
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