According to @godofprompt, after reverse-engineering ChatGPT's memory architecture, it was revealed that the platform does not use sophisticated RAG (Retrieval-Augmented Generation) systems or vector databases for conversation memory. Instead, ChatGPT employs a four-layer system: ephemeral session metadata, explicit long-term user facts, lightweight conversation summaries, and a sliding window of current messages. This architecture avoids embeddings and similarity searches, leading to faster, more efficient context management (source: @godofprompt, Twitter, Dec 10, 2025). The session metadata layer, which includes device type, browser, timezone, and user preferences, is injected per session for real-time adaptation but is not stored permanently, enhancing user experience and privacy. Only 33 explicit long-term facts are stored, focusing on essential user details like name, goals, and preferences, which are added deliberately rather than passively. Recent conversation summaries are stored as lightweight digests, bypassing traditional RAG methods and reducing computational overhead. The sliding window approach for current sessions prioritizes token count over message count, ensuring persistent user context while maintaining performance. This architecture offers significant business opportunities by enabling scalable, privacy-conscious AI applications with superior user adaptation and operational efficiency (source: @godofprompt, Twitter, Dec 10, 2025).