GPT Cache Optimization: A Real-World Case Study This repository documents a real-time cache failure scenario, memory continuity challenge, and optimization workaround—discovered and tested by a general ChatGPT user through hands-on simulation and problem analysis. While working on multi-session GPT simulations, the user encountered persistent PDF generation failures, token overflow loops, and cache redundancy issues. Rather than stop, they measured, analyzed, and proposed a full optimization solution—complete with system behavior logs, trigger-response circuits, and quantifiable metrics. Key Highlights Token reduction metrics after optimization Memory-like routine via user-designed trigger-circuit logic Auto-deletion logic for failed system responses Real system usage scenario with measured performance gains Author Seok Hee-sung, South Korea Additional Notes This report was referenced in official support correspondence with OpenAI and was based on actual system behavior during a real user session.
First seen: 2025-04-20 07:23
Last seen: 2025-04-20 08:23