Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
UX for Enterprise ChatGPT Solutions

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

Arrow left icon
Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835461198
Length 446 pages
Edition 1st Edition
Tools
Arrow right icon
Author (1):
Arrow left icon
Richard H. Miller Richard H. Miller
Author Profile Icon Richard H. Miller
Richard H. Miller
Arrow right icon
View More author details
Toc

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

Prompt engineering techniques

There are dozens of techniques to improve prompts. This section highlights the most valuable strategies for enterprise use cases.

Self-consistency

Think of self-consistency as aligning statements with truth, thus making them logically aligned:

Solar power is a renewable resource. Because solar power is a finite resource, it has unlimited potential.

Solar power is a renewable resource, unlike coal or oil, which have finite reserves. The response from the LLM needs to be more consistent in representing solar power, as it is not a finite resource. The documentation might be an issue, or the context length or writing style infers wrong conclusions. A solution is to provide a few examples that can teach the model. This is not training it with the exact answers; it only gives exemplars to approach the class of problems. It is pretty amazing. Alternatively, ask the question differently and see whether some answers are consistent.

Wang et al. (2023...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image