AI for supplement decisions

The supplement industry sells you the next thing. AI helps you actually test what you already take.

Start the free 10-day challenge →See the method

What we’re actually working with

Supplements are the most marketed and least tested category in personal health. Most people add, never subtract.

Why doing this without a method fails

Influencers and brands sell stacks. Real evidence often disappoints. Most users have never tested whether their stack does anything for them.

The 3-Layer treatment

How the method handles supplements

Layer 01

Research

Have sourced AI read the actual literature on each supplement in your stack — effect size, dose, who responds, who doesn't.

Layer 02

Ledger

Log your current stack with start date, dose, timing, and the symptoms you hoped to change.

Layer 03

Protocol

Run a clean 8-week stop/start test on one supplement at a time. Define success before starting.

Read the full 3-Layer method →

Three prompts you can use today

Paste any of these into the AI chat tool you already use. No setup.

Stack audit

Here's my current supplement stack with doses. For each one: best available evidence, realistic effect size, who responds, and whether you'd keep it. Vendor-neutral, no recommendations to buy other things.

Stop-test design

Help me design an 8-week test where I stop [magnesium / fish oil / creatine] cleanly. Define what I'll measure and what change would count as 'works for me'.

Sourced brief

Give me a 1-page sourced brief on the current evidence for [creatine / NAC / berberine] in healthy adults. Cite each claim.

Tools that help

How AI tools make supplements easier to live with — and understand.

You don’t need another app. These are the tools most people already have or can use for free, and the specific job each one does when you point it at supplements.

Research the literature

A sourced-search AI (e.g. Perplexity, ChatGPT search, Gemini)

Replaces an afternoon of tab-juggling on supplements with a cited summary in minutes. Ask it to mark every claim as primary study, review, or opinion — that one habit removes most of the noise.

Read your own data

A long-memory chat AI (e.g. Claude, ChatGPT, Gemini)

Paste weeks of notes, exports, or symptom logs about supplements in a single window. The AI spots patterns your seven separate apps hide from you, and remembers them next week.

Capture without friction

Apple Health + Notes (or Google Fit + Keep)

Already on your phone. Pulls supplements-relevant signals into one export and lets you jot context in seconds — no new subscription, no new dashboard to maintain.

Stream the raw signal

Your wearable (Oura, Whoop, Garmin, Apple Watch)

Stop reading the marketing score. Export the raw stream behind your supplements number and feed it to a chat AI — that's where the actual insight lives.

Build your own reference

NotebookLM (or any source-grounded notebook)

Drop in your lab PDFs, saved articles, and personal notes on supplements. Ask questions; the answers cite back into your own sources. Becomes a second brain you actually trust.

Turn data into a plan

A weekly review prompt

One scheduled prompt every Sunday: "Given this week's supplements data and notes, what changed, what's noise, what's the smallest experiment for next week?" Replaces three productivity apps and an anxiety spiral.

Browse the AI Tools Atlas →See how the method uses them

Common questions

Will AI tell me what to take?+

No — and that's the point. The method protects you from the next sales pitch.

Is this medical advice?+

No. AI helps you read evidence and your own response. Decisions stay with you and your clinician.

Why doesn't the course recommend brands?+

Because that's how supplement content gets corrupted. Vendor-neutral by design.

Deeper research

The evidence — and where it breaks down

Six short briefs on what the literature, the devices, and the AI tools actually do when you point them at supplements. Read them before you change anything.

What the current research actually says about supplements+

Supplements are the most marketed and least tested category in personal health. Most people add, never subtract. Most peer-reviewed work on supplements sits in three buckets: mechanistic studies (small samples, tightly controlled), observational cohorts (large samples, noisy variables), and consumer-device validation papers (mixed quality, often vendor-funded). When you read AI-generated summaries on AI for supplements, treat the first two as signal and the third as buyer-beware. The 3-Layer method makes you triage these before they enter your personal ledger.

What your wearable or app is really measuring (and what it isn't)+

Consumer devices that surface a "Supplements" score almost always combine a small set of raw signals — accelerometry, optical heart rate, skin temperature, sometimes ECG — into a proprietary index. The score is opinionated, the raw stream is not. The Ledger layer of the method exports the raw stream so AI can analyze the underlying variables instead of the marketing score. That is where most insight lives.

Where consumer-grade supplements data is reliable vs noisy+

Cross-validation studies (Stanford, ETH Zürich, and several EU centres in 2023–2025) consistently show that wearables are most reliable for trend direction and least reliable for absolute values — especially night-to-night supplements. Use the data the way it is actually accurate: deltas over weeks, not single-night verdicts. AI is well-suited to this kind of rolling-window analysis; humans staring at one number are not.

Common confounders that distort supplements signals+

Influencers and brands sell stacks. Real evidence often disappoints. Most users have never tested whether their stack does anything for them. The most under-discussed confounders are time-of-month variation, recent travel, alcohol with a 48–72 hour tail, ambient temperature, and any acute infection — all of which shift baseline values by more than most behaviour changes do. A good AI ledger tags these as covariates before drawing conclusions; a bad one quietly attributes the swing to whatever supplement you started that week.

What "good evidence" looks like — and what's hype+

Good evidence on supplements: pre-registered protocols, declared funding, raw data available, effect sizes reported with confidence intervals, replication in an independent cohort. Hype: single n-of-1 anecdotes generalised on social media, supplement-funded reviews, AI summaries that cite nothing. Have sourced AI read the actual literature on each supplement in your stack — effect size, dose, who responds, who doesn't. Asking AI to mark every claim with "primary study", "review", or "opinion" before you act on it is one of the most useful prompts you can run.

How AI changes the picture for supplements in 2026+

Three shifts matter. First, long-context models can now read 60–90 days of your raw export in a single pass and find correlations no app dashboard surfaces. Second, sourced-search models (with citations) collapse the literature-review step from days to minutes — provided you verify the citations. Third, agentic workflows can run the same daily check-in you would otherwise skip. Run a clean 8-week stop/start test on one supplement at a time. Define success before starting. The judgement layer — what to test, what to ignore, when to stop — is the part that stays with you.

Educational summaries — not medical advice. Cross-check claims against primary sources before changing anything material.