Preface

As large models move from research prototypes into production systems, data has become one of the main limits on model quality. Text, images, video, and audio all require careful acquisition, cleaning, processing, and documentation before they can support reliable training. Web corpora, public datasets, and industry documents are often noisy, duplicated, poorly licensed, or difficult to trace. Turning them into usable training samples therefore requires pipelines that cover crawling, parsing, denoising, deduplication, tokenization, serialization, video frame extraction, audio-video synchronization, multimodal alignment, and annotation.

This book takes data engineering as its central thread. It follows the workflow from data acquisition to data generation for large-scale model training, with attention to distributed data pipelines, task scheduling on heterogeneous hardware, storage optimization, and performance diagnostics. Through chapters on instruction fine-tuning data, preference alignment data, synthetic textbook data, and multimodal instruction data, the book shows how training samples are generated, validated, packaged, and reused. It also covers retrieval-augmented generation systems and enterprise multimodal question-answering systems, connecting dataset construction with application-level engineering. The site edition provides Chinese, English, and Japanese versions so that readers can work with the material in different language settings.

Each chapter combines engineering discussion with concrete cases. The emphasis is on pipeline design, data quality control, versioning, evaluation, reproducibility, and the handoff between research code and deliverable assets. Readers will see why large-model data work is difficult in practice, and how teams can build systems that are scalable, efficient, and reviewable.

The intended readers include academic researchers, industrial engineers, data scientists, platform teams, and students who need to understand how data assets shape model capability. The book is meant to be used both as a reference and as a project companion for building, evaluating, and maintaining large-model data pipelines.