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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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Part 1: Background and Foundational Concepts
7
Part 2: Advanced Design Patterns and Techniques
13
Part 3: GenAI in the Enterprise
19
Index

Advanced Fine-Tuning Techniques and Strategies for Large Language Models

The integration of large language models (LLMs) into business processes represents a significant technological shift, offering massive potential for enhancing productivity, innovation, and customer engagement.

This chapter focuses specifically on strategies for adopting and leveraging LLM technology within enterprises, with an emphasis on fine-tuning pre-trained models and utilizing retrieval-augmented generation (RAG) techniques.

As businesses increasingly recognize the value of generative AI, the demand for AI-powered applications continues to rise. However, the substantial financial costs and technical expertise required to train these models from the ground up aren’t feasible for many organizations. Instead, more accessible approaches, such as utilizing APIs provided by Generative AI leaders like OpenAI, fine-tuning pre-trained models with domain-specific data, and employing intelligent search...

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LLMs in Enterprise
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