Prompt Learning (PL) represents a novel approach to optimizing large language model (LLM) prompts using natural language feedback rather than traditional numerical scores, drawing inspiration from reinforcement learning (RL) but focusing on English instructions to refine prompts. This method, rooted in the Voyager paper and highlighted by Andrej Karpathy, distinguishes itself from conventional prompt optimization by utilizing English error terms to directly adjust instructions, facilitating improvements in scenarios where numerical feedback is inadequate. Unlike RL, which requires numerous examples to optimize model weights, PL leverages individual examples and English annotations to iteratively enhance prompts, making it effective even with fewer data points. This approach allows for continuous online management and adaptation of system prompts, addressing issues such as competing or expiring instructions. The efficacy of PL has been demonstrated through various experiments, including JSON generation tasks and benchmark tests, showing significant improvements with less data. The article highlights PL's potential for continuous AI application improvement, contrasting it with other optimization techniques like PromptAgent, and emphasizing its suitability for both early-stage and production applications.