Prompt Engineering for LLMs
The Art and Science of Building Large Language Model-Based Applications
$141.94
- Paperback
250 pages
- Release Date
19 November 2024
Summary
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 pract…
Book Details
| ISBN-13: | 9781098156152 |
|---|---|
| ISBN-10: | 1098156153 |
| Author: | John Berryman, Albert Ziegler |
| Publisher: | O'Reilly Media |
| Imprint: | O'Reilly Media |
| Format: | Paperback |
| Number of Pages: | 250 |
| Release Date: | 19 November 2024 |
| Weight: | 492g |
| Dimensions: | 233mm x 178mm |
About The Author
John Berryman
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.
Albert Ziegler is a principal machine learning engineer with a PhD in Mathematics and a home at GitHub Next, GitHub’s innovation and future group. His main interests are fusion of deductive and intuitive reasoning to improve the software development experience. At GitHub Next, he was part of the trio that conceived and implemented GitHub Copilot, the first large scale product delivering generative AI for software development. His most recent projects include Copilot Radar and AI for Pull Requests.
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