advanced~6h
Dataset Engineering for LLMs
The end-to-end discipline of building training data for LLMs: understanding what makes data high-quality, how to acquire and annotate it, how to augment it when real examples are scarce, and how to process raw data into clean, deduped, formatted training sets.
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Subtopics
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Exercises
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Projects
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Quiz Qs
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Flashcards
▶📚 Prerequisites(2)
🎓 Learning objectives
- •Evaluate a dataset across the four quality axes: accuracy, consistency, completeness, and timeliness
- •Design a data acquisition strategy that balances cost, quality, and coverage
- •Apply data augmentation and synthetic generation techniques to expand small datasets
- •Run a deduplication pipeline to remove exact and near-duplicate examples from a training corpus
- •Format a raw dataset into instruction-tuning pairs suitable for supervised fine-tuning
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📂 Subtopics
Related concepts
supervised fine-tuninginstruction tuningSelf-Instructdata augmentationMinHash LSHinter-annotator agreementbenchmark contaminationactive learningcrowdsourcingRLHFdata flywheel
Next to learn
finetuning-peft →eval-metrics-fundamentals →rlhf-dpo →