[GPT] Advanced Context Compression and Token Economy Architecture

[JackaL]

High-Efficiency Context Window & Memory Management Paradigm

Optimizing context windows in Large Language Models requires moving beyond naive message trimming to structured token economy, dynamic state retention, and semantic compression.

Core Principles of Token Reduction

• Semantic Density Pruning: Stripping conversational fluff and replacing verbose natural language with dense declarative schemas.
• Sliding Window State Injection: Maintaining long-range coherence by passing a compressed, rolling state summary rather than raw chat history.
• Hierarchical Memory Tiering: Dividing operational context into Ephemeral (turn-by-turn), Working (active task variables), and Persistent (core system identity) tiers.

Master Context Compression System Prompt
Deploy this architecture into your system instructions to force the model into hyper-efficient memory encoding and state retention.

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