The article introduces System Prompt Learning (SPL), an innovative approach enabling Large Language Models (LLMs) to learn and refine problem-solving strategies through practical experience. This method addresses the current disparity where most developers lack the sophisticated system prompts that make advanced AI assistants so capable. SPL represents a "third paradigm" of LLM learning, augmenting traditional pretraining and finetuning by allowing models to classify problems, apply relevant strategies, and continuously improve these strategies over time. The system maintains a dynamic database of human-readable strategies, demonstrating significant performance improvements across various benchmarks and offering benefits like cumulative learning, transparency, and adaptability. Implemented as an open-source plugin in optillm, SPL offers a practical way to integrate this adaptive intelligence into LLM applications.