Data Strategy for LLMs

Learn the essential data techniques for building real-world LLM applications with this practical guide for technical professionals and advanced students, starting from data fundamentals to LLM-specific methods and advanced topics.Key FeaturesLearn state-of-the-art data strategies and techniques for LLM application developmentUnderstand the fundamentals of data collection, processing and synthetic data generationUtilize LLM-specific data techniques for RAG, finetuning and alignment to enhance your LLM applicationGet exposed to multi-modality, pre-training, feedback loops, explainability and more advanced topicsBook DescriptionMost teams building applications with large language models (LLMs) start by choosing a model. Yet the same models are available to everyone, and the one that leads today may be replaced within months. What makes an LLM application reliable is the data around the model: the sources it draws on, the context it receives, the examples it learns from, and the tests that show whether it works. This book walks you through that data lifecycle, from selecting and preparing sources to building a retrieval-augmented generation (RAG) system and fixing the data problems it meets in production. You will apply synthetic data generation to fill coverage gaps, learn what LLM fine-tuning and post-training require, and combine prompts, retrieval, tools, and post-training into one customization process. Examples from four assistant scenarios (personal productivity, domain-specific, enterprise knowledge, and content moderation) show how the same data decision plays out differently in each. You will also create task-specific LLM evaluation data, catch quality problems with automated checks and human oversight, and manage the context that AI agents rely on at runtime. By the end of this book, you will know how to select, prepare, test, and maintain the data that makes LLM applications useful and reliable, with hands-on Python examples you can run and adapt.What you will learnSelect data sources and prepare them as vector-ready knowledgeBuild a RAG system and fix the data problems it hits in productionApply synthetic data generation to fill coverage gapsIdentify the data that LLM fine-tuning, post-training, and alignment requireCustomize models by combining prompts, retrieval, tools, and post-trainingCreate task-specific LLM evaluation datasets and score answers with LLM-as-a-judgeCatch data quality problems with automated checks and human oversightEngineer the context AI agents work from, using the Agentic Repo approachWho this book is forThis book is for AI engineers, scientists, product managers, advanced students and business domain experts looking to deepen their understanding of LLMs and the data techniques for building LLM applications. Basic knowledge of LLMs and the Generative AI landscape is recommended. Whether you are new to the AI space or looking to enhance your AI building skills, this book provides comprehensive guidance on essential data strategies and methods for implementing LLMs in real-world scenarios.]]>