The author reverse-engineered ChatGPT's memory system and found it operates through a four-layer structure rather than complex databases. When queried, ChatGPT recalled 33 specific facts about the author, including personal details, career goals, and fitness routines. The four layers consist of session metadata, long-term user memory, recent conversation summaries, and the current session messages. This design allows ChatGPT to adapt to the user's environment and maintain context without the overhead typical of traditional retrieval systems. Session metadata includes device type, browser information, and usage patterns, but it resets after each session. User memory holds stable, long-term information that the model can recall across sessions. The author noted that memories are stored only when explicitly requested or when ChatGPT identifies relevant facts that the user implicitly acknowledges. Additionally, recent conversation summaries provide a lightweight overview of past interactions, allowing ChatGPT to maintain continuity without the need for complete transcripts. The current session messages maintain the full conversation history, ensuring coherence throughout an ongoing interaction. As the session progresses, older messages roll off, but the system retains memory facts and summaries, balancing performance with personalization. This setup enables ChatGPT to feel aware of the user's preferences and interests without the computational cost associated with searching through extensive message histories. The author highlights that this approach prioritizes efficiency while still offering a personalized experience.