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

Summary

In this chapter, we've embarked on an exploration of LLMs, diving into their historical background, current capabilities, and the common misconceptions that surround these powerful tools. This journey through the development of LLMs not only highlights the technological breakthroughs that have shaped these models but also points toward future advancements and the challenges that lie ahead.

LLMs use an auto-regressive method to predict the next word in a sequence by considering previous words, but this approach has limitations. For instance, the likelihood of errors increases as the sequence lengthens because each prediction carries a chance of error that accumulates over time. Despite their impressive fluency, LLMs cannot truly plan or understand context as humans do, often producing responses that are a mere recombination of learned data without real insight. This is due to their training being limited to existing text, which prevents them from generating novel content or...

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LLMs in Enterprise
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