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The Role of AI in Supplement Recommendations

Unfair Team • January 5, 2026

AI is most useful in supplements when it explains each recommendation and learns from your logs over time.

Why it matters

Recommendation quality depends on both evidence rules and personal response feedback. Rules keep safety and dose ranges inside known limits, and feedback tunes ranking based on your outcomes. This approach avoids static advice that ignores your actual day to day response.

How to apply it

Treat AI output as a starting point, then review rationale before every stack change. Keep note quality high so model updates have useful input rather than sparse mood tags. Reject any recommendation that cannot explain dose, expected effect, and risk context.

In Unfair

Unfair ties recommendation cards to dose events and journal responses, so each future suggestion can reference what happened in your own history.

Continue with How AI Personalizes Supplement Recommendations, Evaluating AI Supplement Recommendations, AI-Assisted Dose Logging Workflows.