This book delves into a broad spectrum of topics, covering the foundational aspects of Large Language Models (LLMs) such as PaLM, LLaMA, BERT, and GPT, among others.

The book takes you through the complexities involved in creating and deploying applications based on LLMs, providing you with an in-depth understanding of the model architecture. You will explore techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG). The book also addresses different ways to evaluate LLM outputs and discusses the benefits and limitations of large models. The book focuses on the tools, techniques, and methods essential for developing Large Language Models. It includes hands-on examples and tips to guide you in building applications using the latest technology in Natural Language Processing (NLP). It presents a roadmap to assist you in navigating challenges related to constructing and deploying LLM-based applications.

By the end of the book, you will understand LLMs and build applications with use cases that align with emerging business needs and address various problems in the realm of language processing.

What You Will Learn

• Be able to answer the question: What are Large Language Models?
• Understand techniques such as prompt engineering, fine-tuning, RAG, and vector databases
• Know the best practices for effective implementation
• Know the metrics and frameworks essential for evaluating the performance of Large Language Models

Who This Book Is For

An essential resource for AI-ML developers and enthusiasts eager to acquire practical, hands-on experience in this domain; also applies to individuals seeking a technical understanding of Large Language Models (LLMs) and those aiming to build applications using LLMs

Table of Contents

Chapter 1: Introduction to Large Language Models

Chapter 2: Understanding Foundation Models

Chapter 3: Adapting with Fine-Tuning

Chapter 4: Magic of Prompt Engineering

Chapter 5: Stop Hallucinations with RAG

Chapter 6: Evaluation of LLMs

Chapter 7: Frameworks for Development

Chapter 8: Run in Production

Chapter 9: The Ethical Dilemma

Chapter 10: The Future of Al

About the Author

Bhawna Singh, a Data Scientist at CeADAR (UCD), holds both a bachelor and master degree in computer science. During her master’s program, she conducted research focused on identifying gender bias in Energy Policy data across the European Union. With prior experience as a Data Scientist at Brightflag in Ireland and a Machine Learning Engineer at AISmartz in India, Bhawna brings a wealth of expertise from both industry and academia. Her current research interests center on exploring diverse applications of Large Language Models. Over the course of her career, Bhawna has built models on extensive datasets, contributing to the development of intelligent systems addressing challenges such as customer churn, propensity prediction, sales forecasting, recommendation engines, customer segmentation, pdf validation, and more. She is dedicated to creating AI systems that are accessible to everyone, promoting inclusivity regardless of race, gender, social status, or language.