Data Engineering for Large Language Models: Architecture, Algorithms and Projects
By
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If you only eat one bagel today, this is the bagel.
Summary
This is a technical book about data engineering for large language models (LLMs), covering the complete technical stack from pre-training data cleaning to multimodal alignment, RAG retrieval augmentation, and synthetic data generation. The book aims to fill the gap in systematic resources for LLM data engineering, addressing the critical role of data quality in determining model performance in the era of large models.
Key quotes
· 5 pulledData is the new oil, but only if you know how to refine it.
In the era of large models, data quality determines the upper bound of model performance.
Yet systematic resources on LLM data engineering remain extremely scarce — most teams are still learning by trial and error.
This book is designed to fill that gap.
We systematically cover the complete technical stack from pre-training data cleaning to multimodal alignment, from RAG retrieval augmentation to synthetic data generation.
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