Coding agents play a pivotal role in modern software development, with tools like Cursor, Claude Code, Codex, and Cline enhancing how engineers write and ship code by using a single, persistent system prompt to maintain state and continuity in coding tasks. The precision and scope of this system prompt significantly impact the agent's performance, and developers can fine-tune agent behavior by appending user-defined rules to the system prompt. However, creating effective rules is challenging, leading to the application of an optimization algorithm called Prompt Learning, which refines rule files and improves coding agent accuracy without retraining underlying models. This approach was applied to Cline, an open-source coding agent, resulting in a 10-15% accuracy improvement as measured by SWE Bench, a benchmark used to evaluate the resolution of real GitHub issues. Prompt Learning employs a meta prompting technique to enhance prompts, using input-output pairs and detailed evaluations to generate improved rulesets that generalize and strengthen agent performance across diverse cases.