• #48 - The Art of Prompt Engineering with Andrew Mayne (ex-OpenAI Creative Apps and Science Communicator)

  • Dec 6 2023
  • Length: 3 hrs and 12 mins
  • Podcast

#48 - The Art of Prompt Engineering with Andrew Mayne (ex-OpenAI Creative Apps and Science Communicator)

  • Summary

  • Discussing Prompt Engineering and recent OpenAI developments with ex-OpenAI Creative Apps and Scientific Communicator Andrew Mayne Timestamps: 00:00:00 - Teaser Reel Intro 00:01:01 - Intro / Andrew's background 00:02:49 - What was it like working at OpenAI when you first joined? 00:12:59 - Was Andrew basically one of the earliest Prompt Engineers? 00:14:04 - How Andrew Hacked his way into a tech job at OpenAI 00:17:08 - Parallels between Hollywood and Tech jobs 00:20:58 - Parallels between the world of Magic and working at OpenAI 00:25:00 - What was OpenAI like in the Early Days? 00:30:24 - Why it was hard promoting GPT-3 early on 00:31:00 - How would you describe the current 'instruction age' of prompt design? 00:35:22 - What was GPT-4 like freshly trained? 00:39:00 - Is there anything different about the raw base model without RLHF? 00:42:00 - Optimizations that go into Language models like GPT-4 00:43:30 - What was it like using DALL-E 3 very early on? 00:44:38 - Do you know who came up with the 'armchair in the shape of an avocado' prompt at OpenAI? 00:45:48 - Did you experience 'DALL-E Dreams' as a part of the DALL-E 2 beta? 00:47:16 - How else has prompt design changed? 00:49:27 - How has prompt design changed because of ChatGPT? 00:52:40 - How to get ChatGPT to mimick and emulate personalities better? 00:54:30 - Mimicking Personalities II (How to do Style with ChatGPT) 00:56:40 - Fine Tuning ChatGPT for Mimicking Elon Musk 00:59:44 - How do you get ChatGPT to come up with novel and brilliant ideas? 01:02:40 - How do you get ChatGPt to get away from conventional answers? 01:05:14 - Will we ever get single-shot, real true novelty from LLM's? 01:10:05 - Prompting for ChatGPT Voice Mode 01:12:20 - Possibilities and Prompting for GPT-4 Vision 01:15:45 - GPT-4 Vision Use Cases/Startup Ideas 01:21:37 - Does multimodality make language models better or are the benefits marginal? 01:24:00 - Intuitively, has multimodality improved the world model of LLM's like GPT-4? 01:25:33 - What would it take for ChatGPT to write half of your next book? 01:29:10 - Qualitatively, what would it take to convince you about a book written by AI? What are the characteristics? 01:31:30 - Could an LLM mimick Andrew Mayne's writing style? 01:37:49 - Jailbreaking ChatGPT 01:41:12 - What's the next era of prompt engineering? 01:45:50 - How have custom instructions changed the game? 01:54:41 - How far do you think we are from asking a model how to make 10 million dollars and getting back a legit answer? 02:01:07 - Part II - Making Money with LLM's 02:11:32 - How do you make a chat bot more reliable and safe? 02:12:12 - How do you get ChatGPT to consistently remember criteria and work within constraints? 02:12:45 - What about DALL-E? How do you get it to better create within constraints? 02:14:14 - What's your prompt practice like? 02:15:10 - Do you intentionally sit down and practice writing prompts? 02:16:45 - How do you build an intuition around prompt design for an LLM? 02:20:00 - How do you like to iterate on prompts? Do you have a process? 02:21:45 - How do you know when you've hit the ceiling with a prompt? 02:24:00 - How do you know a single line prompt is has room to improve? 02:26:40 - Do you actually need to know OpenAI's training data? What are some ways to mitigate this? 02:30:40 - What are your thoughts on automated prompt writing/optimization? 02:33:20 - How do you get a job as a prompt engineer? What makes a top tier prompt engineer different from an everyday user? 02:37:20 - How do you think about scaling laws a prompt engineer? 02:39:00 - Effortless Prompt Design 02:40:52 - What are some research areas that would get you a job at OpenAI? 02:43:30 - The Research Possibilities of Optimization & Inference 02:45:59 - If you had to guess future capabilities of GPT-5 what would they be? 02:50:16 - What are some capabilities that got trained out of GPT-4 for ChatGPT? 02:51:10 - Is there any specific capability you could imagine for GPT-5? Why is it so hard to predict them? 02:56:06 - Why is it hard to predict future LLM capabilities? (Part II) 02:59:47 - What made you want to leave OpenAI and start your own consulting practice? 03:05:29 - Any remaining advice for creatives, entrepreneurs, prompt engineers? 03:09:25 - Closing Subscribe to the Multimodal By Bakz T. Future Podcast! Spotify - https://open.spotify.com/show/7qrWSE7ZxFXYe8uoH8NIFV Apple Podcasts - https://podcasts.apple.com/us/podcast/multimodal-by-bakz-t-future/id1564576820 Google Podcasts - https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkLnBvZGJlYW4uY29tL2Jha3p0ZnV0dXJlL2ZlZWQueG1s Stitcher - https://www.stitcher.com/show/multimodal-by-bakz-t-future Other Podcast Apps (RSS Link) - https://feed.podbean.com/bakztfuture/feed.xml Connect with me: YouTube - https://www.youtube.com/bakztfuture Substack Newsletter - https://bakztfuture.substack.com​ Twitter - https://www.twitter.com/bakztfuture​ Instagram - https://www.instagram.com/...
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