Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based ApplicationsКНИГИ » ПРОГРАММИНГ
Название: Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications Автор: John Berryman, Albert Ziegler Издательство: O’Reilly Media, Inc. Год: 2025 Страниц: 282 Язык: английский Формат: True/Retail PDF, True/Retail EPUB Размер: 22.5 MB
Large language models (LLMs) are revolutionizing the world, promising to automate tasks and solve complex problems. A new generation of software applications are using these models as building blocks to unlock new potential in almost every domain, but reliably accessing these capabilities requires new skills. This book will teach you the art and science of prompt engineering-the key to unlocking the true potential of LLMs. Industry experts John Berryman and Albert Ziegler share how to communicate effectively with AI, transforming your ideas into a language model-friendly format. By learning both the philosophical foundation and practical techniques, you'll be equipped with the knowledge and confidence to build the next generation of LLM-powered applications. Understand LLM architecture and learn how to best interact with it Design a complete prompt-crafting strategy for an application Gather, triage, and present context elements to make an efficient prompt Master specific prompt-crafting techniques like few-shot learning, chain-of-thought prompting, and RAG.
In Part I of the book, we convey a foundational understanding of LLMs, their inner workings, and their functionality as text completion engines. We cover the extension of LLMs to their new role as chat engines, and we present a high-level approach to LLM application development. In Part II, we introduce the core techniques for prompt engineering—how to source context information, rank its importance for the task at hand, pack the prompt (without overloading it), and organize everything into a template that will result in high-quality completions that elicit the answer you need. In Part III, we move to more advanced techniques. We assemble loops, pipelines, and workflows of LLM inference to create conversational agency and LLM-driven workflows, and we then explain techniques for evaluating LLMs.
Who Is This Book For? This book is written for application engineers. If you build software products that customers use, then this book is for you. If you build internal applications or data-processing workflows, then this book is also for you. The reason that we are being so inclusive is because we believe that the usage of LLMs will soon become ubiquitous. Even if your day-to-day work doesn’t involve prompt engineering or LLM workflow design, your codebase will be filled with usages of LLMs, and you’ll need to understand how to interact with them just to get your job done.
However, a subset of application engineers will be the dedicated LLM wranglers—these are the prompt engineers. It’s their job to convert problems into a packet of information that the LLM can understand—which we call the prompt—and then convert the LLM completions back into results that bring value to those who use the application. If this is your current role—or if you want this to be your role—then this book is especially for you.
LLMs are very approachable—you speak with them in natural language. So, for this book, you won’t be expected to know everything about Machine Learning. But you do need to have a good grasp of basic engineering principles—you need to know how to program and how to use an API. Another prerequisite for this book is the ability to empathize, because unlike with any technology before, you need to understand how LLMs “think” so that you can guide them to generate the content you need. This book will show you how.
Preface I. Foundations 1. Introduction to Prompt Engineering 2. Understanding LLMs 3. Moving to Chat 4. Designing LLM Applications II. Core Techniques 5. Prompt Content 6. Assembling the Prompt 7. Taming the Model III. An Expert of the Craft 8. Conversational Agency 9. LLM Workflows 10. Evaluating LLM Applications 11. Looking Ahead Index
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Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications 
