Mitigating Context Window Pollution in LLM-Assisted Software Engineering via Dual-Memory Interfaces

Abstract As Large Language Models (LLMs) become integral to multi-stage software development, current linear conversational interfaces exhibit a critical structural limitation: Context Window Pollution. During extensive programming projects, developers frequently utilize the primary chat thread for iterative debugging, error resolution, and transient conceptual queries. This elongates the conversation history, severely degrading the model’s attention mechanism and causing a loss of focus on the overarching project architecture. We propose a Dual-Memory Context Architecture that introduces an Auxiliary Side Panel (Working Memory) alongside the Main Workspace (Long-Term Memory). This panel possesses asymmetric, read-only access to the main context but maintains an isolated conversational history. By segregating transient debugging noise from core architectural signals, this paradigm significantly optimizes token consumption, mitigates hallucination risks, and preserves the model’s macro-architectural awareness throughout complex workflows.