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James Phoenix - Co-Author of “Prompt Engineering for Generative AI” @jamesaphoenix
Mike Taylor - Co-Author of “Prompt Engineering for Generative AI”
Phil Winder - Author of “Reinforcement Learning” & CEO of Winder.AI @philwinder263
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.postgresql.org
https://www.mongodb.com
https://dreambooth.github.io
https://github.com/microsoft/LoRA
https://claude.ai
https://www.langchain.com/langgraph
https://cursordevkit.com
https://youtu.be/W-JX9j8idpQ
https://youtu.be/Pv0cfsastFs
https://youtu.be/MznD2DzlQCc
https://youtu.be/vIGIQZmwcqo
https://youtu.be/NDWdkmvX91E
https://youtu.be/la4kc57F6jU
https://youtu.be/Yk0_LWd0WQA
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
* Book description: © O'Reilly:
https://www.oreilly.com/library/view/prompt-engineering-for/9781098153427
TIMECODES
00:00 Intro
02:27 Key aspects of Prompt Engineering & its definition
08:22 Misconceptions about Prompt Engineering
10:21 Practical applications & tools
13:43 5 principles of prompting
26:27 Balancing creativity & specificity in AI models
32:42 RAG: The power of iterative steps, query planning & routing in AI systems
42:59 Understanding agentic systems: Beyond prompt-based AI
51:56 Outro
RECOMMENDED BOOKS
James Phoenix & Mike Taylor • Prompt Engineering for Generative AI • https://amzn.to/43cFDqO
Phil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZ
Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • https://amzn.to/3AQmIRg
Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • https://amzn.to/3m9HNjP
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