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

View More author details

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

Why subscribe?

17. Other Books You May Enjoy

The essentials of prompt engineering

Before discussing prompt engineering, it is important to first understand the foundational components of a prompt. In this section, we’ll delve into the key components of a prompt, such as ChatGPT prompts, completions, and tokens. Additionally, grasping what tokens are is pivotal to understanding the model’s constraints and managing costs.

ChatGPT prompts and completions

A prompt is an input provided to LLMs, whereas completions refer to the output of LLMs. The structure and content of a prompt can vary based on the type of LLM (e.g., the text or image generation model), specific use cases, and the desired output of the language model.

Completions refer to the response generated by ChatGPT prompts; basically, it is an answer to your questions. Check out the following example to understand the difference between prompts and completions when we prompt ChatGPT with, “What is the capital of India?”

...