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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 FREE CHAPTER
2. Chapter 1: Recognizing the Power of Design in ChatGPT 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

Monitoring and Evaluation

Once there is something to test, even a trial version, be on top of the processes for evaluating the results. Unsurprisingly, the methods discussed (surveys, interviews, feedback) can be re-used to see how beta customers or early adopters perform.

Another Pet Peeve

The word beta sends the wrong message to a non-technical customer that the product is not ready for them. Consider other terms such as limited release or, my favorite, access for early adopters. This label might put them in a better frame of mind to handle issues and provide feedback.

Since retrieval-augmented generation (RAG) is fundamental to most enterprise solutions for sales and support, metrics around the quality of that approach are essential. A combination of data science, product managers, and the design team is required to improve results. A heuristic approach using design experts or trained individuals can evaluate RAG or other LLM outputs that provide results to customers.

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