Looking at your publication I notice this structured data representation between the LLM and the prescription. We found that adding an Ontology to the LLM reduced hallucination from 92% (GPT 5) to 8% when mapping from Clinical terms to SNOMED.

Good reminder, Theo. Equally important is the practice of setting rates too high. As pointed out by a recent OIG report charges exceeding 200% of the national average are a "Red Flag" for audit risk. fairpath.ai/resources/rpm-gu… See our Fraud Risk Report for more info.

This isn't just about healthcare. It's a blueprint for all "grounded symbolic mapping" tasks: • Legal docs → Statutes • Cyber reports → CVEs • Product → Taxonomies • Science → Gene Ontologies

L3 (Autonomous Agent): The LLM could now search, navigate the @snomedct hierarchy, and refine its own results. Final Accuracy: 79.48% (Exact) & 89.48% (Looser). This is the accurate, verifiable, and production-ready result.

The results scaled with autonomy: * L1 (Selector): 55.48% (vs 53.91% baseline). Proved the LLM is a smarter tie-breaker. * L2 (Expander): Jumped to 63.91%. We used the LLM to generate synonyms ("HFpEF") before searching to solve the recall problem.

Our solution: Ontology-Guided Augmented Retrieval (OGAR). We stopped giving the LLM an open-ended essay and gave it a multiple-choice test. We transformed the task from generative recall to constrained reasoning.

We first asked the models to recall the code from "memory." The results were a catastrophe. • @OpenAI GPT-5: 8.43% • GPT-5-mini: 5.30% • @xai Grok-4: 1.22% • @xai Grok-4-fast: 0.26% They all hallucinated non-existent codes. This "memory-only" approach is unusable.

The task: map 1,150 real clinical terms—shorthand like “AAA” or “HFpEF”—to precise @snomedct medical codes. This is a high-stakes precision task. Get it right = analytics, billing, safety. Get it wrong = chaos.

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