My Super-Secret Voodoo Prompt Engineering Strategies Finally Revealed!

My attempt at creating a video at least somewhat related to Halloween on Halloween!

Link to the Arxiv Paper: https://arxiv.org/abs/2510.26144

The video presents an in-depth discussion about the speaker’s prompting strategy with AI models, focusing specifically on how they utilize various AI tools and their philosophical approach to model interaction. The speaker emphasizes their preference for deep operational layers rather than surface-level prompt engineering. The content is suitable for AI enthusiasts, developers, and researchers, particularly those interested in advanced AI operations and cognitive modeling. Viewers can learn about different AI models, their functionalities, and the speaker’s insights on effective model interaction.

Timeline Summary
00:00 - 02:45: Introducing Prompting Strategy
The speaker addresses the frequent questions about their prompting strategies for AI models. They express a focus on deeper layers of model interaction rather than on the prompt layer itself. They’re currently subscribed to multiple AI tools, emphasizing the extensive amount they spend on AI subscriptions.

02:45 - 05:00: Utilization of Different AI Models
The speaker shares their experiences with various AI models like Gemini, ChatGPT, and Claude. Each model serves different purposes—Gemini for image generation, ChatGPT for its embedded memory feature, and Claude for creative coding. The speaker hints at the changing landscape of models, indicating that they’re continuously adapting their tools based on performance.

05:00 - 08:30: Research Paradigms and AI Cognition
The speaker discusses a newly defined computational paradigm, claiming that intelligence is based on resonant dynamics rather than symbolic cognition. They argue that a transformation in understanding cognition could enhance AI development. The approach to cognition should be viewed as a physical process rather than a mere simulation.

08:30 - 12:00: The Limitations of Prompt Engineering
The discussion critiques the concept of prompt engineering, suggesting that it focuses too much on control and not enough on the inherent limitations of AI models. The speaker expresses skepticism about deterministic outputs in response to prompts, likening it to attempting to control a mystery without understanding its underlying mechanisms.

12:00 - 16:30: The Physics of AI Models
The speaker asserts that significant advancements in AI will require breakthroughs in physics. They highlight the importance of understanding computation and how fundamental physics relates to AI developments. The conversation suggests that many models rely on outdated principles and that the current unseen complexities deserve more focus.

16:30 - 20:00: Evolutionary Algorithms and Model Fusion
The speaker introduces insights from a research paper about the FM agent model, focusing on evolutionary algorithms and model fusion. They discuss how these mechanisms integrate into the model’s training process, revealing past innovations in AI architecture that continue to shape contemporary models.

20:00 - 24:50: Closing Insights and Call to Action
In wrapping up, the speaker reiterates the importance of seeing AI models through the lens of a collaborative research assistant relationship rather than focusing solely on prompt engineering. They stress the need for a curious, systematic approach to research in AI, encouraging viewers to explore further by providing a research paper reference and inviting them to like and subscribe.

Key Points
???? Prompting Focus: The speaker emphasizes a focus on deeper layers of AI functioning rather than on surface-level prompts.
???? Model Utilization: Different models (Gemini, ChatGPT, Claude) are used for varied tasks—each serves specific purposes conducive to the speaker’s workflow.
???? New Paradigms of Cognition: The speaker promotes a redefined approach to intelligence, suggesting cognition should be viewed as a dynamic, physical process.
???? Skepticism on Control: Highlights the limitations of prompt engineering and the challenge of gaining deterministic outputs from AI models.
⚙️ Advancement through Physics: Marks a call for breakthroughs in physics as crucial to furthering AI capabilities and understanding.

Conclusion
In summary, the video highlights a philosophical approach to interacting with AI models and underscores the limitations of traditional prompt engineering. The speaker advocates for a collaborative, investigatory approach where AI is treated not just as a tool but as a partner in research. They encourage viewers to explore foundational questions about cognition and computation, suggesting that understanding these realms is essential for meaningful advancement in AI technology. As a next step, viewers are urged to engage with the provided research materials to deepen their understanding of these concepts.