In an exploration of prompt optimization techniques, the blog post details how Claude Code, a leading coding agent using the Claude Sonnet 4-5 model, was enhanced using Prompt Learning, an approach inspired by reinforcement learning. This method focuses on optimizing the system prompts of coding agents based on their performance in handling datasets, specifically using SWE Bench Lite, a benchmark for evaluating coding models. Through a structured process involving meta-prompting and LLM feedback, Claude Code's performance improved significantly, achieving a 5.19% increase in general coding abilities and a 10.87% boost when tailored to specific repositories. This demonstrates the effectiveness of refining prompts without altering model architectures or tools, highlighting the potential of personalized, repository-specific optimization as a valuable asset for developers.