You're reading from Generative AI for Cloud Solutions Architect modern AI LLMs in secure, scalable, and ethical cloud environments

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781835084786

Length 300 pages

Edition 1st Edition

Languages

Python

Tools

ChatGPT

Concepts

GPT/LLMs

Authors (2):

Paul Singh

Anurag Karuparti

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Table of Contents (18) Chapters

Preface

1. Part 1:Integrating Cloud Power with Language Breakthroughs FREE CHAPTER

2. Chapter 1: Cloud Computing Meets Generative AI: Bridging Infinite Impossibilities

3. Chapter 2: NLP Evolution and Transformers: Exploring NLPs and LLMs

4. Part 2: Techniques for Tailoring LLMs

5. Chapter 3: Fine-Tuning – Building Domain-Specific LLM Applications

6. Chapter 4: RAGs to Riches: Elevating AI with External Data

7. Chapter 5: Effective Prompt Engineering Techniques: Unlocking Wisdom Through AI

8. Part 3: Developing, Operationalizing, and Scaling Generative AI Applications

9. Chapter 6: Developing and Operationalizing LLM-based Apps: Exploring Dev Frameworks and LLMOps

10. Chapter 7: Deploying ChatGPT in the Cloud: Architecture Design and Scaling Strategies

11. Part 4: Building Safe and Secure AI – Security and Ethical Considerations

12. Chapter 8: Security and Privacy Considerations for Gen AI – Building Safe and Secure LLMs

13. Chapter 9: Responsible Development of AI Solutions: Building with Integrity and Care

14. Part 5: Generative AI – What’s Next?

15. Chapter 10: The Future of Generative AI – Trends and Emerging Use Cases

16. Index

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Real-life examples of fine-tuning success

In this section, we’ll explore a real-life example of a fine-tuning approach that OpenAI implemented, which yielded remarkable outcomes.

InstructGPT

OpenAI’s InstructGPT is one of the most successful stories of fine-tuned models that laid the foundation of ChatGPT. ChatGPT is said to be a sibling model to InstructGPT. The methods that are used to fine-tune ChatGPT are similar to InstructGPT. InstructGPT was created by fine-tuning pre-trained GPT-3 models with RHLF. Supervised fine-tuning is the first step in RLHF for generating responses aligned to human preferences.

In the beginning, GPT-3 models weren’t originally designed to adhere to user instructions. Their training focused on predicting the next word based on vast amounts of internet text data. Therefore, these models underwent fine-tuning using instructional datasets along with RLHF to enhance their ability to generate more useful and relevant responses...