This repository, titled GPT Cache Optimization, presents an in-depth case study examining real-time cache failure and memory continuity challenges faced during multi-session GPT simulations. Authored by Seok Hee-sung, an 18-year-old student from South Korea, this documentation details the hands-on simulation and extensive problem analysis conducted to address these issues.

During the project, the user encountered significant obstacles such as persistent PDF generation failures, token overflow loops, and cache redundancy problems. Rather than abandon the effort, a comprehensive optimization solution was developed, encompassing system behavior logs, trigger-response circuits, and quantifiable performance metrics.

Key Highlights

• Token Reduction Metrics: Detailed insights into token reduction achieved through optimization strategies.
• Memory-Like Routine: Innovative user-designed trigger-circuit logic that simulates memory functionality.
• Auto-Deletion Logic: Mechanism implemented for the automatic deletion of failed system responses, enhancing efficiency.
• Real System Usage Scenario: A documented case showcasing measurable performance gains from the optimization effort.

This case study is an important contribution to understanding cache issues in GPT applications and has been referenced in official support communication with OpenAI, reflecting actual system behaviors observed in user sessions.