{ "item_title" : "Data Strategy for LLMs", "item_author" : [" Eslam Kamal", "Rany Elhousieny", "Yash Sheth "], "item_description" : "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 Features: - Learn state-of-the-art data strategies and techniques for LLM application development- Understand the fundamentals of data collection, processing and synthetic data generation- Utilize LLM-specific data techniques for RAG, finetuning and alignment to enhance your LLM application- Get exposed to multi-modality, pre-training, feedback loops, explainability and more advanced topicsBook Description: Most 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 Learn: - Select data sources and prepare them as vector-ready knowledge- Build a RAG system and fix the data problems it hits in production- Apply synthetic data generation to fill coverage gaps- Identify the data that LLM fine-tuning, post-training, and alignment require- Customize models by combining prompts, retrieval, tools, and post-training- Create task-specific LLM evaluation datasets and score answers with LLM-as-a-judge- Catch data quality problems with automated checks and human oversight- Engineer the context AI agents work from, using the Agentic Repo approachWho this book is for: This 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.Table of Contents- Getting Started with LLMs: From First API Call to Data Strategy- Data Sources- Data Preparation: From Raw Data to Vector-Ready Knowledge- Building Your First RAG System: From Theory to Implementation- Data Strategy for Advanced RAG: When Your Data Tells You What You Need- Synthetic Data Generation- Understanding LLM Post Training- LLM Model Customization- Task-Specific Evaluation Data- Data Quality and Human Oversight- Context Engineering: Data Strategy at Agent Runtime", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/80/611/699/1806116995_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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