Today, I'm intrigued by a thought-provoking question from our guest, Carlos Perez:
"Can we lay the map for concepts that are beyond human comprehension?"
It's a question that perfectly encapsulates our journey into the world of AI and learning.
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Joining us on today's podcast is author Carlos Perez, known for his insightful works like The Deep Learning Playbook, and Artificial Intuition, Fluency & Empathy. But today, we're zooming in on his book that's particularly close to my heart, "Design Patterns for Generative AI."
Carlos's book has been a constant source of fascination for me. Think of it as a Rosetta Stone for crafting better prompts to derive more meaningful results from a Large Language Model (LLM); not just using an LLM to boost productivity. Carlos delves deeper, exploring how it can expand our creativity and deepen our understanding of complex topics. This approach resonates with me, as I believe enhancing our creative and intellectual horizons is far more significant than mere productivity gains.
The concepts in his book have empowered me to confidently explore new topics which has enhanced my learning and writing. By understanding the patterns underlying broad subjects, I can approach them in a more creative and organized way. It's not just about learning faster; it's about attaining a richer, more profound understanding.
Thanks to Carlos, I now feel emboldened to craft my prompts instead of relying on standard, pre-made ones that often fall short of expectations.
Let's dive into a conversation with Carlos to uncover the motivations behind writing this book and gain insights into his process of designing patterns for Generative AI.
Let's get started:
Carlos, I'm curious, what inspired you to take this pattern language approach for designing better prompts for LLMs? What was your journey like?
Podcast Summary with Carlos Perez:
Carlos explains how asking GPT-4 to reveal its patterns led to the genesis of his book on prompt engineering. He differentiates prompting for thinking versus automation, provides advice for beginners on asking good questions, envisions combinatorial prompt patterns as composable languages, notes GPT’s counterintuitive ability to infer causes from effects, speculates smaller AI firms have secret sauce training methods, and looks ahead to future interactive versions of his book that are more self-generative.
Key points for this podcast:
1. Carlos originally tried to create a pattern language for deep learning 5 years ago, but it was too complex and evolving, so he gave up on that project.
2. The idea for his current book on GPT prompt patterns came after playing with GPT-4 and asking it to reveal some of its own patterns, which expanded to 70 different patterns.
3. Carlos distinguishes prompt engineering for automation from prompting to get the most out of using GPT-4 as a thinking tool. Beginners need guidance on how to ask good questions.
4. Combinatorial use of prompt patterns allows fusing concepts together, like languages that become composable into new vocabularies.
5. GPT's ability to determine a cause from a given effect seems counterintuitive and backward but is related to abductive reasoning.
6. Smaller AI firms may have secret sauce training methods that let their models outperform much bigger models from giants like Google.
7. Carlos envisions his book becoming more self-generating with GPT's larger context windows, where you can query the book interactively about prompt patterns.
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- To understand more see The History of Pattern Languages by Christopher Alexander who developed the idea of Pattern Languages in their 1977 book by the same name. Recommended!
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To find out more about A Pattern Language for Generative AI: A Self-Generating GPT-4 Blueprint visit here:
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