Prompt Engineering for LLMs (for Hoang Anh) | 81 | John Berryman and Albert Ziegler | O'Reilly Media, Inc |
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.
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
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