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Prompt engineering in the enterprise...

Koch, Daniel.

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Prompt engineering in the enterprise - an introduction = competitive advantages through generative AI and large language models /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prompt engineering in the enterprise - an introduction/ by Daniel Koch, Andreas Kohne, Nils Brechbühler.
Reminder of title:	competitive advantages through generative AI and large language models /
Author:	Koch, Daniel.
other author:	Kohne, Andreas.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2025.,
Description:	xxii, 124 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Fundamentals: Large Language Models and Generative Pre-trained Transformers, Tokens and Context Windows -- Prompt Engineering: Concept, Evaluation, Formulation, Formats -- Frameworks for Prompts: including Prompt Chaining, Role Prompting, R-T-F, T-A-G, B-A-B, C-A-R-E, R-I-S-E -- Advanced Techniques: Zero-, One-, and Few-Shot Prompts, Cognitive Verifier Patterns, Chain-of-Thought Prompts -- Metaprompting: Foundations and Approaches -- AI Agents: Concept, Architectures, Use Cases.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Business applications. -
Online resource:	https://doi.org/10.1007/978-3-658-49218-2
ISBN:	9783658492182

Prompt engineering in the enterprise - an introduction = competitive advantages through generative AI and large language models /
Koch, Daniel.

Prompt engineering in the enterprise - an introductioncompetitive advantages through generative AI and large language models /[electronic resource] :by Daniel Koch, Andreas Kohne, Nils Brechbühler. - Wiesbaden :Springer Fachmedien Wiesbaden :2025. - xxii, 124 p. :ill. (some col.), digital ;24 cm.

Fundamentals: Large Language Models and Generative Pre-trained Transformers, Tokens and Context Windows -- Prompt Engineering: Concept, Evaluation, Formulation, Formats -- Frameworks for Prompts: including Prompt Chaining, Role Prompting, R-T-F, T-A-G, B-A-B, C-A-R-E, R-I-S-E -- Advanced Techniques: Zero-, One-, and Few-Shot Prompts, Cognitive Verifier Patterns, Chain-of-Thought Prompts -- Metaprompting: Foundations and Approaches -- AI Agents: Concept, Architectures, Use Cases.

This book provides a step-by-step introduction to the art of prompt engineering - the most essential skill for the efficient use of generative AI. Since the introduction of ChatGPT at the latest, Large Language Models (LLMs) have become ubiquitous. These systems deliver impressive answers to a wide range of questions, draft concepts and texts, translate, program, and handle tasks. However, the true added value in everyday business only unfolds when the questions are asked correctly. This is because the quality of the answers generated by an LLM depends primarily on the quality of the questions. From the Contents Fundamentals: Large Language Models and Generative Pre-trained Transformers, Tokens and Context Windows Prompt Engineering: Concept, Evaluation, Formulation, Formats Frameworks for Prompts: including Prompt Chaining, Role Prompting, R-T-F, T-A-G, B-A-B, C-A-R-E, R-I-S-E Advanced Techniques: Zero-, One-, and Few-Shot Prompts, Cognitive Verifier Patterns, Chain-of-Thought Prompts The Authors Daniel Koch, Dr. Andreas Kohne and Nils Brechbühler are managing directors of Valisory GmbH, a consulting firm that supports its clients on the path of digital transformation. Currently, they advise public sector clients as well as private enterprises in the field of artificial intelligence. In addition, they are active as university lecturers. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation.

ISBN: 9783658492182

Standard No.: 10.1007/978-3-658-49218-2doiSubjects--Topical Terms:

3785910
Artificial intelligence
--Business applications.

LC Class. No.: HF5548.2

Dewey Class. No.: 658.0563

Prompt engineering in the enterprise - an introduction = competitive advantages through generative AI and large language models /
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520 $a This book provides a step-by-step introduction to the art of prompt engineering - the most essential skill for the efficient use of generative AI. Since the introduction of ChatGPT at the latest, Large Language Models (LLMs) have become ubiquitous. These systems deliver impressive answers to a wide range of questions, draft concepts and texts, translate, program, and handle tasks. However, the true added value in everyday business only unfolds when the questions are asked correctly. This is because the quality of the answers generated by an LLM depends primarily on the quality of the questions. From the Contents Fundamentals: Large Language Models and Generative Pre-trained Transformers, Tokens and Context Windows Prompt Engineering: Concept, Evaluation, Formulation, Formats Frameworks for Prompts: including Prompt Chaining, Role Prompting, R-T-F, T-A-G, B-A-B, C-A-R-E, R-I-S-E Advanced Techniques: Zero-, One-, and Few-Shot Prompts, Cognitive Verifier Patterns, Chain-of-Thought Prompts The Authors Daniel Koch, Dr. Andreas Kohne and Nils Brechbühler are managing directors of Valisory GmbH, a consulting firm that supports its clients on the path of digital transformation. Currently, they advise public sector clients as well as private enterprises in the field of artificial intelligence. In addition, they are active as university lecturers. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation.
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