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LLMs in Enterprise

LLMs in Enterprise

By : Ahmed Menshawy, Mahmoud Fahmy
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LLMs in Enterprise

LLMs in Enterprise

By: Ahmed Menshawy, Mahmoud Fahmy

Overview of this book

The integration of large language models (LLMs) into enterprise applications is transforming how businesses use AI to drive smarter decisions and efficient operations. LLMs in Enterprise is your practical guide to bringing these capabilities into real-world business contexts. It demystifies the complexities of LLM deployment and provides a structured approach for enhancing decision-making and operational efficiency with AI. Starting with an introduction to the foundational concepts, the book swiftly moves on to hands-on applications focusing on real-world challenges and solutions. You’ll master data strategies and explore design patterns that streamline the optimization and deployment of LLMs in enterprise environments. From fine-tuning techniques to advanced inferencing patterns, the book equips you with a toolkit for solving complex challenges and driving AI-led innovation in business processes. By the end of this book, you’ll have a solid grasp of key LLM design patterns and how to apply them to enhance the performance and scalability of your generative AI solutions. *Email sign-up and proof of purchase required
Table of Contents (20 chapters)
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1
Part 1: Background and Foundational Concepts
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7
Part 2: Advanced Design Patterns and Techniques
13
Part 3: GenAI in the Enterprise
19
Index

Background and Foundational Concepts

In Part 1 of this book, we build a solid foundation by introducing the core concepts of large language models (LLMs) and their strategic role in the enterprise. We explore how these models are transforming business processes and identify the key challenges they present. This part makes a strong case for mastering LLM design patterns to ensure scalability, security, and success..

This part contains the following chapters:

  • Chapter 1, Introduction to Large Language Models
  • Chapter 2, LLMs in Enterprise: Applications, Challenges, and Design Patterns
  • Chapter 3, Advanced Fine-Tuning Techniques and Strategies for Large Language Models
  • Chapter 4, Retrieval-Augmented Generation Pattern
  • Chapter 5, Customizing Contextual LLMs
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Tech Concepts
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Programming languages
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LLMs in Enterprise
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