GPT Cache Optimization: A Real-World Case Study
2025-04-20
A South Korean user encountered persistent PDF generation failures, token overflow loops, and cache redundancy issues while running multi-session GPT simulations. Instead of giving up, they meticulously measured, analyzed, and implemented an optimization solution involving system behavior logs, trigger-response circuits, and quantifiable metrics. The optimization significantly reduced token usage, implemented a memory-like routine via custom trigger-circuit logic, and automated the deletion of failed system responses. This report, based on real user session data, was referenced in official correspondence with OpenAI.