Cognitive Engineering: Designing Optimal Representations for Human-AI Knowledge Interfaces

The evolution of AI interaction design has progressed through three stages: prompt engineering (what words to send), context engineering (what information to include), and intent engineering (why decisions were made). Each stage addressed a more fundamental question than the last, yet none address the question that determines whether the information actually lands: what shape should it take for a specific cognitive consumer to process it most accurately? We define Cognitive Engineering as the discipline of designing optimal information representations matched to consumer cognition. We formalize this as a four-density-layer model — Prose, Structure, Binary, Logic — where a single source document projects to the representation tier that matches the consumer's cognitive architecture. We evaluate this framework against 15 systems drawn from an initial survey of 40+ candidates. No prior system combines all five required properties: dual-layer structure, consumer-aware projection, density optimization, typed semantic edges, and single source. Scientific justification comes from Context Rot research (Chroma, 2025), which demonstrates that more input tokens degrade AI output quality — establishing that density optimization is not merely an efficiency play but an accuracy intervention. Patent Support: Flagship paper supporting 9-patent portfolio. All patents filed March 30, 2026 (USPTO App# 64/022,405 through 64/022,480).