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UX for Enterprise ChatGPT Solutions

UX for Enterprise ChatGPT Solutions

By : Richard H. Miller
4.6 (7)
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UX for Enterprise ChatGPT Solutions

UX for Enterprise ChatGPT Solutions

4.6 (7)
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

Fine-Tuning

What happens when prompt engineering efforts have gone as far as they can go? If higher quality results are still needed, examples are overwhelming the prompt, performance issues appear, or token costs are excessive because of a large prompt, fine-tuning comes into the picture.

As mentioned in the last chapter, solutions sometimes require overlapping approaches such as Retrieval-Augmented Generation (RAG), prompt engineering, and fine-tuning. Fine-tuning helps the model improve its understanding. We will focus on a few critical deliverables before contextualizing them by completing the Wove case study started in Chapter 6, Gathering Data – Content is King:

  • Fine-tuning 101
  • Creating fine-tuned models
  • Fine-tuning tips
  • Wove case study, continued

Regardless of the tools, the team must care and feed the large language model (LLM) to improve the output. Though the methods discussed in the book can reach limits, fine-tuning is another excellent...

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