Andrej Karpathy has introduced a new paradigm in large language model (LLM) training called "System Prompt Learning." This approach diverges from traditional methods such as pretraining, which focuses on knowledge acquisition, and fine-tuning, which adjusts habitual behavior by changing model parameters. Instead, System Prompt Learning emphasizes explicit, high-dimensional feedback mechanisms that mimic how humans note and recall information. This method leverages the power of well-crafted prompts to enhance AI performance without altering the underlying model parameters. The concept is inspired by recent developments, including the revelation of an extensive system prompt containing 16,739 words used by Claude, another AI model. Experts argue that tuning system prompts is the simplest and most effective way to train AI, outperforming more complex agentic systems. This paradigm shift aligns with growing recognition that as AI models improve, prompt engineering becomes increasingly important for guiding AI to perform complex tasks. Additionally, new research highlights that mastering prompt engineering techniques can improve AI output quality by up to 35%. These advances are part of broader innovations in AI, including applications like LeanDojo, which uses AI for mathematical theorem proving, further pushing the boundaries of structured reasoning and AI capabilities.