The internet is full of wellness advice built on a single cherry-picked study. Renee Deehan, a molecular and cell biologist who leads science and AI at InsideTracker, spent two decades building the opposite. In this episode, she explains why the core of their recommendation engine is symbolic AI, knowledge representation and reasoning, rather than a large language model: it's deterministic, fully auditable, and by design cannot hallucinate. The LLMs are fenced off to chat and summaries, while humans still write and review every recommendation against convergent clinical evidence. She and the hosts dig into a 20,000-user outcomes study, the discipline of refusing to claim causality, the MCT-oil case where the system decides not to recommend, and how a data-science team grew its own AI literacy instead of hiring it.