03853nam a22005415i 4500 979-8-8688-0569-1 DE-He213 20251030114555.0 cr nn 008mamaa 241130s2024 xxu| s |||| 0|eng d 9798868805691 979-8-8688-0569-1 10.1007/979-8-8688-0569-1 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Singh, Bhawna. author. aut http://id.loc.gov/vocabulary/relators/aut 88322 Building Applications with Large Language Models [electronic resource] : Techniques, Implementation, and Applications / by Bhawna Singh. 1st ed. 2024. Berkeley, CA : Apress : Imprint: Apress, 2024. XVII, 280 p. 149 illus., 126 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter 1: Introduction to Large Language Models -- Chapter 2: Understanding Foundation Models -- Chapter 3: Adapt with Fine-tuning -- Chapter 4: The Magic of Prompt Engineering -- Chapter 5: Stop Hallucination with RAG -- Chapter 6: Evaluation of LLM -- Chapter 7: Tools and Frameworks for Development -- Chapter 8: Run in Production.-Chapter 9: The Ethical Dilemma -- Chapter 10: Future of AI. 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 . Artificial intelligence. 58441 Machine learning. 55059 Python (Computer program language). 64608 Natural language processing (Computer science). 65131 Artificial Intelligence. 58441 Machine Learning. 55059 Python. 47553 Natural Language Processing (NLP). 66164 SpringerLink (Online service) Springer Nature eBook Printed edition: 9798868805684 Printed edition: 9798868805707 https://doi.org/10.1007/979-8-8688-0569-1 ZDB-2-CWD ZDB-2-SXPC 65362 65362