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  • Book Overview & Buying UX for Enterprise ChatGPT Solutions
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UX for Enterprise ChatGPT Solutions

UX for Enterprise ChatGPT Solutions

By : Richard H. Miller
4.7 (6)
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UX for Enterprise ChatGPT Solutions

UX for Enterprise ChatGPT Solutions

4.7 (6)
By: Richard H. Miller

Overview of this book

Many enterprises grapple with new technology, often hopping on the bandwagon only to abandon it when challenges emerge. This book is your guide to seamlessly integrating ChatGPT into enterprise solutions with a UX-centered approach. UX for Enterprise ChatGPT Solutions empowers you to master effective use case design and adapt UX guidelines through an engaging learning experience. Discover how to prepare your content for success by tailoring interactions to match your audience’s voice, style, and tone using prompt-engineering and fine-tuning. For UX professionals, this book is the key to anchoring your expertise in this evolving field. Writers, researchers, product managers, and linguists will learn to make insightful design decisions. You’ll explore use cases like ChatGPT-powered chat and recommendation engines, while uncovering the AI magic behind the scenes. The book introduces a and feeding model, enabling you to leverage feedback and monitoring to iterate and refine any Large Language Model solution. Packed with hundreds of tips and tricks, this guide will help you build a continuous improvement cycle suited for AI solutions. By the end, you’ll know how to craft powerful, accurate, responsive, and brand-consistent generative AI experiences, revolutionizing your organization’s use of ChatGPT.
Table of Contents (18 chapters)
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1
Part 1:UX Foundation for Enterprise ChatGPT
7
Part 2: Designing
11
Part 3: Care and Feeding

Gathering Data – Content is King

There is an assumption in this book: enterprise ChatGPT solutions are needed in almost all cases because a company has something unique to offer its customers, and it possesses an exceptional understanding of its products, services, and content. This content is private or unique and thus not part of large language models (LLMs) built from scraping the internet. Models are built on crawling the 2+ billion pages of web content to teach the model. A third party, Commoncrawl.org, is commonly cited as a primary source of this material for major models (GPT-3, Llama). These models, which are massive collections of text, learn the statistical relationships of words and concepts and can be used to predict and respond to questions. Creating a model can take months; most have billions of connections and words. When customers come to the enterprise for answers, the models must include enterprise content that is not part of this crawl to make them unique...

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