This paper addresses the challenge of incomplete knowledge coverage in large language models, particularly in specialized and data-scarce domains. The authors propose SPA (Scaling Prompt-engineered Augmentation), a method that uses carefully designed prompts to generate large-scale synthetic training data for knowledge injection. Through systematic comparisons, SPA demonstrates superior performance over strong baselines. The research identifies key limitations in existing approaches: RL-based methods suffer from diversity collapse at scale, while multi-stage prompting advantages disappear after careful tuning. These findings provide valuable insights for optimizing knowledge injection strategies in language models.