03458nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001300153050001400166072001600180072002300196072001500219100007600234245014400310250001800454264005400472300006800526336002600594337002600620338003600646347002400682505040000706520147101106650002902577650002202606650004002628650005202668650002902720650002202749650001202771650003902783710003402822773002602856776003602882776003602918856004602954979-8-8688-0569-1DE-He21320251030114555.0cr nn 008mamaa241130s2024    xxu|    s    |||| 0|eng d  a97988688056917 a10.1007/979-8-8688-0569-12doi 4aQ334-342 4aTA347.A78 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aSingh, Bhawna.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aBuilding Applications with Large Language Modelsh[electronic resource] :bTechniques, Implementation, and Applications /cby Bhawna Singh.  a1st ed. 2024. 1aBerkeley, CA :bApress :bImprint: Apress,c2024.  aXVII, 280 p. 149 illus., 126 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aChapter 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.  aThis 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 . 0aArtificial intelligence. 0aMachine learning. 0aPython (Computer program language). 0aNatural language processing (Computer science).14aArtificial Intelligence.24aMachine Learning.24aPython.24aNatural Language Processing (NLP).2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z979886880568408iPrinted edition:z979886880570740uhttps://doi.org/10.1007/979-8-8688-0569-1