AI Book Club: Prompt Engineering for Generative AI

July's book is "Prompt Engineering for Generative AI"!

This is a casual-style audio discussion event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back.

Want to discuss the contents during the rest of the month? Join Flyte MLOps Slack group and search for the "ai-book-club" channel. https://slack.flyte.org/

Feel free to join the discussion even if you have not read the book chapters! :)
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About the book:
Title: Prompt Engineering for Generative AI
Authors: James Phoenix, Mike Taylor
Published: May 2024

Read at your own pace, but my goal will be these chapters per week:

Discussion Week 1 (7/9):
1. The Five Principles Of Prompting
2. Introduction To Large Language Models For Text Generation
Discussion Week 2 (7/16):
3. Standard Practices For Text Generation With ChatGPT
4. Advanced Techniques For Text Generation With LangChain
5. Vector Databases With FAISS And Pinecone
Discussion Week 3 (7/23):
6. Autonomous Agents With Memory And Tools
7. Introduction To Diffusion Models For Image Generation
8. Standard Practices For Image Generation With Midjourney
Discussion Week 3 (7/30):
9. Advanced Techniques For Image Generation With Stable Diffusion
10. Building AI-Powered Applications

Discuss as you read in the ai-book-club channel: https://slack.flyte.org/

Book Description:
Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation.
With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI.
Learn how to empower AI to work for you. This book explains:

• The structure of the interaction chain of your program's AI model and the fine-grained steps in between
• How AI model requests arise from transforming the application problem into a document completion problem in the model training domain
• The influence of LLM and diffusion model architecture—and how to best interact with it
• How these principles apply in practice in the domains of natural language processing, text and image generation, and code

Learn more about the book here:
https://learning.oreilly.com/library/view/prompt-engineering-for/9781098153427/