Programming Large Language Models with Azure Open AI: Conversational programming and prompt engineering with LLMs

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Title Page....1 Copyright Page....2 Dedication....5 Contents at a Glance....6 Contents....7 Acknowledgments....11 Introduction....13 Who should read this book....14 Assumptions....15 This book might not be for you if…....15 Organization of this book....15 Downloads: notebooks and samples....16 Errata, updates, & book support....16 Stay in touch....17 Chapter 1. The genesis and an analysis of large language models....18 LLMs at a glance....19 Facts of conversational programming....39 Summary....53 Chapter 2. Core prompt learning techniques....54 What is prompt engineering?....54 Basic techniques....69 Fundamental use cases....85 LLM limitations....91 Summary....91 Chapter 3. Engineering advanced learning prompts....93 What’s beyond prompt engineering?....94 Function calling....101 Talking to (separated) data....112 Summary....132 Chapter 4. Mastering language frameworks....134 The need for an orchestrator....135 LangChain....148 Microsoft Semantic Kernel....182 Microsoft Guidance....199 Summary....212 Chapter 5. Security, privacy, and accuracy concerns....213 Overview....214 Security and privacy....220 Evaluation and content filtering....234 Summary....253 Chapter 6. Building a personal assistant....254 Overview of the chatbot web application....255 The project....257 Summary....285 Chapter 7. Chat with your data....286 Overview....286 What is Streamlit?....288 The project....295 Progressing further....313 Summary....318 Chapter 8. Conversational UI....319 Overview....320 The project....324 Summary....340 Appendix. Inner functioning of LLMs....341 The role of probability....341 The case of GPT....356 Index....368 Code Snippets....395
Описание
Коротко и по делу о том, что важно знать про language.
Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Insight into prompting and conversational programming―with specific techniques for patterns and frameworks―unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input.
Artificial Intelligence expert Francesco Esposito helps you:Understand the history of large language models and conversational programmingApply prompting as a new way of codingLearn core prompting techniques and fundamental use-casesEngineer advanced prompts, including connecting LLMs to data and function calling to build reasoning enginesUse natural language in code to define workflows and orchestrate existing APIsMaster external LLM frameworksEvaluate responsible AI security, privacy, and accuracy concernsExplore the AI regulatory landscapeBuild and implement a personal assistantApply a retrieval augmented generation (RAG) pattern to formulate responses based on a knowledge baseConstruct a conversational user interfaceFor IT Professionals and ConsultantsFor software professionals, architects, lead developers, programmers, and Machine Learning enthusiastsFor anyone else interested in natural language processing or real-worldapplications of human-like language in software
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автор — Esposito Francesco, издательство Microsoft Press, год выпуска 2024, 605 страниц.
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Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural.