In this thought-provoking episode, hosts Bradley and Justin explore the intriguing concept of using AI to write prompts for other AI systems. They delve into the paradoxical nature of this idea and discuss potential approaches to make it feasible.The hosts emphasize the importance of having a structured system and well-defined schemas when attempting to automate prompt creation. They argue that abstract prompt generation without context is impractical for real-world applications. Instead, they propose leveraging existing prompts and system knowledge to guide AI in creating new, relevant prompts.Bradley and Justin examine various methods for AI-assisted prompt writing, including fine-tuning models on existing prompts and using few-shot learning approaches. They stress the need for human oversight in the process, likening LLMs to three-year-olds that require clear examples and guidance.The conversation touches on the challenges of prompt iteration and version control, with Justin suggesting the potential for developing tools to manage prompt versions effectively. The hosts also briefly discuss optimization approaches and the complexities of chaining multiple prompts together.Throughout the episode, Bradley and Justin provide a pragmatic perspective on the future of AI-assisted prompt engineering, balancing enthusiasm for innovation with realistic expectations about current AI capabilities. Listeners will gain valuable insights into the nuanced world of prompt creation and the potential for AI to augment this process in meaningful ways.---Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.Check out PromptDesk.ai for an open-source prompt management tool.Check out Brad’s AI Consultancy at bradleyarsenault.meAdd Justin Macorin and Bradley Arsenault on LinkedIn.Please fill out our listener survey here to help us create a better podcast: https://docs.google.com/forms/d/e/1FAIpQLSfNjWlWyg8zROYmGX745a56AtagX_7cS16jyhjV2u_ebgc-tw/viewform?usp=sf_linkHosted on Ausha. See ausha.co/privacy-policy for more information.

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