Prompt Engineering for LLMs - Berryman, John; Ziegler, Albert
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Erscheint vorauss. 31. Dezember 2024
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Large language models (LLMs) promise unprecedented benefits. Well versed in common topics of human discourse, LLMs can make useful contributions to a large variety of tasks, especially now that the barrier for interacting with them has been greatly reduced. Potentially, any developer can harness the power of LLMs to tackle large classes of problems previously beyond the reach of automation. This book provides a solid foundation of LLM principles and explains how to apply them in practice. When first integrating LLMs into workflows, most developers struggle to coax useful insights from them.…mehr

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Produktbeschreibung
Large language models (LLMs) promise unprecedented benefits. Well versed in common topics of human discourse, LLMs can make useful contributions to a large variety of tasks, especially now that the barrier for interacting with them has been greatly reduced. Potentially, any developer can harness the power of LLMs to tackle large classes of problems previously beyond the reach of automation. This book provides a solid foundation of LLM principles and explains how to apply them in practice. When first integrating LLMs into workflows, most developers struggle to coax useful insights from them. That's because communicating with AI is different from communicating with humans. This guide shows you how to present your problem in the model-friendly way called prompt engineering. With this book, you'll: * Examine the user-program-AI-user model interaction loop * Understand the influence of LLM architecture and learn how to best interact with it * Design a complete prompt crafting strategy for an application that fits into the application context * Gather and triage context elements to make an efficient prompt * Formulate those elements so that the model processes them in the way that's desired * Master specific prompt crafting techniques including few-shot learning, and chain-of-thought prompting
Autorenporträt
John Berryman started out in Aerospace Engineering but soon found that he was more interested in math and software than in satellites and aircraft. He soon switched to software development, specializing in search and recommendation technologies, and not too long afterward co-authored Relevant Search. At GitHub John played a prominent role in moving code search to a new scalable infrastructure. Subsequently John joined the Data Science team, and then Copilot where he currently provides technical leadership and direction in Prompt Crafting work.