Formulation-based consumer health products, such as dietary supplements, functional foods, drink mixes, and topical personal-care formulations, are widely used, repeatedly consumed, and often trusted by consumers to deliver specific functional outcomes. In the safety-sensitive context of formulation decisions, informal and externally driven heuristics, such as trending ingredients, supplier stock formulations, or formulator intuition, often guide the process rather than a structured, evidence-constrained design approach. This article presents an evidence-first design perspective for formulation-based consumer health systems and introduces the Evidence-First Functional Formulation Design (EFFFD) framework, which reorders formulation decisions around explicit problem definition, functional intent specification, hypothesis-level reasoning about how formulation components are expected to contribute to the stated functional intent, and evidence-constrained design. Artificial intelligence supports evidence discovery, dosage-range aggregation, interaction analysis, and safety risk screening, with expert review limited to a final pre-production feasibility and safety check. By formalizing upstream design logic and separating functional intent from market-driven heuristics, EFFFD increases the likelihood of functional effectiveness while improving transparency, reproducibility, and scientific defensibility in safety-sensitive formulation design.