Prompt Engineering for Generative AI • James Phoenix, Mike Taylor & Phil Winder

This interview was recorded for the GOTO Book Club.
http://gotopia.tech/bookclub

Read the full transcription of the interview here
(https://gotopia.tech/episodes/360)
James Phoenix - Co-Author of "Prompt Engineering for Generative AI"
Mike Taylor - Co-Author of "Prompt Engineering for Generative AI"
Phil Winder - Author of "Reinforcement Learning" & CEO of Winder.AI

RESOURCES
James
https://x.com/jamesaphoenix12
(https://github.com/jamesaphoenix) https://www.linkedin.com/in/jamesphoenix
https://understandingdata.com

Mike
http://saxifrage.xyz
https://twitter.com/hammer_mt
https://www.linkedin.com/in/mjt145

Phil
https://twitter.com/DrPhilWinder
https://linkedin.com/in/drphilwinder
(https://github.com/philwinder) https://winder.ai
(https://winder.ai/blog)
Links
https://brightpool.dev
https://karpathy.ai
https://help.openai.com/en/articles/6654000
https://gemini.google.com
(https://www.mongodb.com) https://dreambooth.github.io
https://github.com/microsoft/LoRA
https://claude.ai
https://www.langchain.com/langgraph

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 the
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