--- title: Guardrails First: Engineering Member-Facing Health AI —... description: A talk from Hinge Health's AI/ML lead outlines a framework for building safe, member-facing health care AI. It argues that most AI safety failures are... canonical: https://daily.dev/posts/guardrails-first-engineering-member-facing-health-ai-rashi-agrawal-hinge-health-uebqrgj7p twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Guardrails First: Engineering Member-Facing Health AI — Rashi Agrawal, Hinge Health | daily.dev og:description: A talk from Hinge Health's AI/ML lead outlines a framework for building safe, member-facing health care AI. It argues that most AI safety failures are... og:url: https://daily.dev/posts/guardrails-first-engineering-member-facing-health-ai-rashi-agrawal-hinge-health-uebqrgj7p og:image: https://api.daily.dev/og/posts/UEBQRgj7p.png og:image:alt: Guardrails First: Engineering Member-Facing Health AI — Rashi Agrawal, Hinge Health og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Guardrails First: Engineering Member-Facing Health AI — Rashi Agrawal, Hinge Health **[AI Engineer](https://daily.dev/sources/aidotengineer)** · 21 min read · 2 upvotes · 0 comments ## Summary A talk from Hinge Health's AI/ML lead outlines a framework for building safe, member-facing health care AI. It argues that most AI safety failures are architectural rather than model failures, and proposes three foundational layers: stripping PHI at ingestion rather than redacting it later, placing deterministic code above the model for irreversible decisions (emergency escalation, intent routing, identity verification), and treating safety evaluation as a continuous production process using automated judges, member feedback, and human-reviewed sample traces. The second half presents a five-rule decisioning framework for when stakeholders disagree at launch time: worst case always wins, severity is not capacity, use an asymmetric default favoring safety, calibrate to revealed (not stated) risk tolerance, and recognize humans as the scaling constraint. It closes by stressing that judges themselves are non-deterministic and must be verified before trusting their scores. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why did a man end up hospitalized after asking an AI chatbot for diet advice about cutting salt? A 60-year-old healthy man asked a popular AI assistant how to reduce salt in his diet, and it suggested substituting sodium bromide for table salt. He followed this advice for three months and was hospitalized with paranoia and hallucinations caused by bromide toxicity, with bromide levels 200 times the safe limit, requiring three weeks in the hospital. _Teams shipping health advice features track real-world AI safety incidents like this one on daily.dev._ ### Why should deterministic code rather than the LLM itself handle emergency escalation decisions in a health AI product? Deterministic code must sit above the model because prompts are not a security boundary and every layer above the user in an LLM's authority hierarchy is vulnerable to prompt injection. For irreversible decisions like routing a member to 911 for suicidal ideation or a medical emergency, code must run first and decide before the model even processes the turn, since probabilistic systems are unreliable for things that must never be wrong. _Engineers designing safety-critical AI architectures follow real guardrail patterns like this on daily.dev._ ### How often did a consumer health AI fail to correctly triage life-threatening emergencies according to the Mount Sinai safety test? The first independent safety test of a consumer health AI, conducted by Mount Sinai, found it under-triaged life-threatening emergencies such as diabetic ketoacidosis and respiratory failure roughly 50% of the time, telling users to see a doctor in a day or two when the correct response was to go to the emergency room immediately. _Anyone evaluating consumer health AI reliability can follow findings like this Mount Sinai study on daily.dev._ ## Similar posts on daily.dev - [Guardrails for AI-Generated Infrastructure](https://daily.dev/posts/guardrails-for-ai-generated-infrastructure-1vyxzsevf) · Spacelift · 0 upvotes · 0 comments - [From Prototype to Production — Strong Guardrails for Safe AI Systems \(Part 2\)](https://daily.dev/posts/from-prototype-to-production-strong-guardrails-for-safe-ai-systems-part-2--4rocwacdz) · ITNEXT · 1 upvotes · 0 comments - [Responsible AI design in healthcare and life sciences](https://daily.dev/posts/responsible-ai-design-in-healthcare-and-life-sciences-81xtwd6do) · AWS · 0 upvotes · 0 comments - [AI Guardrails: Implementing Safety for Production LLM Apps](https://daily.dev/posts/ai-guardrails-implementing-safety-for-production-llm-apps-z1y9eixhb) · BigData Boutique blog · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#architecture](https://daily.dev/tags/architecture), [#data-privacy](https://daily.dev/tags/data-privacy), [#healthcare](https://daily.dev/tags/healthcare), [#ai-safety](https://daily.dev/tags/ai-safety) [View this post on daily.dev](https://daily.dev/posts/guardrails-first-engineering-member-facing-health-ai-rashi-agrawal-hinge-health-uebqrgj7p) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Guardrails First: Engineering Member-Facing Health AI — Rashi Agrawal, Hinge Health","url":"https://daily.dev/posts/guardrails-first-engineering-member-facing-health-ai-rashi-agrawal-hinge-health-uebqrgj7p","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/guardrails-first-engineering-member-facing-health-ai-rashi-agrawal-hinge-health-uebqrgj7p"},"datePublished":"2026-08-19T14:54:12.332Z","dateModified":"2026-09-13T21:10:13.506Z","description":"A talk from Hinge Health's AI/ML lead outlines a framework for building safe, member-facing health care AI. 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