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Workbook

Dismissal-Based Learning Playbook

5-page playbook · negative-signal architecture, scoring, caching, anonymous handoff, analytics

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A 5-page workbook on building a recommendation engine that learns from negative signals. Covers the full live architecture powering Suggested for you on wellnessand.ai: dismissal capture, affinity decay with 14-day half-life, anonymous-to-authenticated handoff via the claim_session_dismissals RPC, server-side scoring weights, instant card replacement, three-layer caching with versioned invalidation, and the six analytics events that prove it works (cache hit/miss rate, dismissal rate, replacement latency, re-suggestion guardrail, anonymous-to-auth handoff rate). Includes code snippets, SQL health-check query, and a rollout checklist.

Who it's for

For practitioners scaling 1:1 work without scaling burnout.

Best for

  • You run a coaching, nutrition, functional-medicine or longevity practice.
  • You want to use AI as a delivery layer — not replace your clinical judgement.
  • You're done re-explaining the same protocol in every consult.

Not the right fit if

  • You want a fully managed software product — this is a build-it-yourself system.
  • You're not ready to put your method in writing.

Audience: Practitioners · Domain: ai

🔒 The step-by-step guide unlocks after purchase.

Before you buy.

It's built for practitioners — coaches, nutritionists, integrative clinicians — who want to give clients a private AI workflow without recommending another consumer app.

Find the next layer of your stack.