Название: The LLM Mesh: A Practical Guide to Using Generative AI in the Enterprise (Early Release)
Автор: Kurt Muehmel
Издательство: O’Reilly Media, Inc.
Год: 2024-08-02
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

In the rapidly evolving landscape of Generative AI and particularly large language models (LLMs), organizations are poised to create groundbreaking applications. However, the lack of oversight, governance, and centralization often stymies the deployment of LLM-based applications. For organizations eager to harness the full potential of LLMs, overcoming these hurdles is essential—not only for operational success but also to stay ahead in a competitive market.

This tech guide introduces the LLM Mesh, an architecture paradigm that ensures modular development, centralized administration, and auditing of LLM applications. It allows companies not only to keep pace with rapid changes and new market entrants but also to maintain independence from any single provider. Through detailed instructions and expert advice, learn how to abstract applications from LLM services, integrate robust security measures, manage compliance, and optimize costs and performance across various platforms. By the end of this guide, you will:

Understand the enterprise challenges associated with building LLM-powered applications
Recognize the benefits of implementing an LLM Mesh within large organizations
Learn comprehensive strategies for the safe, secure, and scalable utilization of LLMs
Select appropriate LLMs and hosting methods tailored to specific applications

An LLM Mesh architecture focuses on LLMs and not Generative AI more broadly because LLMs are the core building blocks of the AI applications that will be built in the enterprise. LLMs are large neural networks trained on text data. They possess a variety of natural language processing capabilities. Many, but not all, LLMs can generate text. Generative AI is a broader category of AI that includes models that can generate text, audio, images, and videos. Beyond simply generating text, LLMs are also used to reason through a problem, to give instructions to various tools, and to write the code to connect to various tools. While image-generating models, for example, can be useful in the enterprise, they are not relevant in the context of building sophisticated AI applications that are the focus of the LLM Mesh.