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 topics
Book Description
Artificial Intelligence applications are thriving, and Large Language Models (LLMs) are at the center of this rapid growth. Practical data strategy and techniques are essential for successful development of LLMs and usually define the performance of your AI application. This book walks you through the LLM data techniques starting from the data fundamentals, moving into the specific LLM data techniques for typical training and inference pipelines and ending with advanced LLM applications and data management topics. Throughout the book, it walks you through the data strategy and methods required for building LLM-powered AI assistants for the common AI assistant scenarios: personal, enterprise, domain-specific and content moderation assistants. You'll learn practical data management and training and inference data techniques that are widely applicable to different LLMs, technologies and clouds. By the end of this book, you will be proficient in data collection, generation and management techniques for LLM application throughout the development lifecycle spanning training, evaluation, inference and continuous improvement. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will sharpen your LLM application development abilities.
What you will learn
- Basics of LLM-powered AI Assistant system and architecture
- Fundamental data collection and processing for ML applications
- Synthetic data generation and augmentation methods and applications
- RAG and CAG benefits and their training and inference data needs
- Practical data strategy for LLM post-training, evaluation and alignment
- Approaches for data quality, human-in-the-loop and building SLMs
- Basics of LLM pre-training, multimodal LLMs and LLM explainability
- Understand Data Management and Governance
- Monitoring Bias, Fairness and Toxicity
Who 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.
