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UX for Enterprise ChatGPT Solutions

You're reading from   UX for Enterprise ChatGPT Solutions A practical guide to designing enterprise-grade LLMs

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835461198
Length 446 pages
Edition 1st Edition
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Author (1):
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Richard H. Miller Richard H. Miller
Author Profile Icon Richard H. Miller
Richard H. Miller
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Table of Contents (18) Chapters Close

Preface 1. Part 1:UX Foundation for Enterprise ChatGPT
2. Chapter 1: Recognizing the Power of Design in ChatGPT FREE CHAPTER 3. Chapter 2: Conducting Effective User Research 4. Chapter 3: Identifying Optimal Use Cases for ChatGPT 5. Chapter 4: Scoring Stories 6. Chapter 5: Defining the Desired Experience 7. Part 2: Designing
8. Chapter 6: Gathering Data – Content is King 9. Chapter 7: Prompt Engineering 10. Chapter 8: Fine-Tuning 11. Part 3: Care and Feeding
12. Chapter 9: Guidelines and Heuristics 13. Chapter 10: Monitoring and Evaluation 14. Chapter 11: Process 15. Chapter 12: Conclusion 16. Index 17. Other Books You May Enjoy

Getting started with conversational analysis

The final type of research we want to cover is conversational analysis. We don’t have a ChatGPT solution yet, so how can I have anything to analyze? Maybe not, but let’s consider other sources. Are there existing trouble tickets or service requests? Maybe there are live-person chats or phone channels. Even reviewing community posts in forums is a good source. Perhaps an existing conversational AI doesn’t include ChatGPT integration. This is an excellent source of existing data. Conversational analysis isn’t valuable for backend solutions or even recommendation systems. Use other research and needs analysis tools there.

At one company, we kept metrics on the quantity and type of support calls. Unsurprisingly, password reset requests and account activations were broken out. We precisely knew the percentage of calls for these services and could then calculate savings to a change in a process that improved that...

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