The NKB Cognitive Pipeline: A Four-Stage Framework for Knowledge-Environment-Conditioned AI Generation

This paper presents the NKB Cognitive Pipeline, a four-stage framework (User Prompt, Intent Vector, Context Tensor, Generation) for knowledge-environment-conditioned AI generation. We define two novel constructs — the intent vector (a six-dimensional decomposition of user intent) and the context tensor (a multi-dimensional alignment between intent and knowledge graph structure) — and present benchmark evidence that naive RAG degrades model performance in training-dense domains. We argue that knowledge systems for AI should be designed as inhabitable environments rather than retrievable document stores. Developed by William Daniel Raper with Claude Opus 4.6 (Anthropic) operating as The Professor within the Neural Knowledge Base.