The Brain as RAG: How We Think, Decide, and Learn (govindchavada.substack.com)

A clinician‑author frames the brain as a biological Retrieval‑Augmented Generation (RAG) system: perception is the prompt, hippocampus and association cortices act as the retriever, prefrontal working memory stages retrieved “pages,” and neocortex functions as the predictive generator that writes the next action or belief. Emotion gates retrieval (an “affect vector”), meta‑cognition and source memory provide guardrails, and consolidation (hippocampal replay, synaptic strengthening, schema formation) builds the searchable index. The piece maps specific neural substrates to RAG components and shows why this analogy clarifies fast, context‑sensitive decisions in clinic and everyday life. Technically rich and pragmatic, the model explains common failure modes—hallucinations from poor retrieval or overconfident generation, tunnelled search under stress, and stale memories—and prescribes fixes analogous to ML practice: broaden retrieval, down‑regulate arousal, refresh indexes, and enforce citation‑style metacognition. It also highlights multimodal retrieval (visual, auditory, interoceptive) and actionable learning strategies (deliberate practice, contrastive learning, reflection). For AI/ML researchers and clinicians, the framing suggests cross‑fertilization: build better guardrails and interpretability into models, and use RAG‑inspired training to sharpen human judgment and reduce diagnostic overconfidence.