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	<title>#Healthcare Generative AI &#8211; Santeware | Engineering Healthcare Data</title>
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	<description>&#124; Meeting Requirements &#124; Exceeding Expectations</description>
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		<title>Beyond the Prompt: Building Deterministic Guardrails for GenAI in Clinical Workflows</title>
		<link>https://santeware.com/beyond-the-prompt-building-deterministic-guardrails-for-genai-in-clinical-workflows/</link>
					<comments>https://santeware.com/beyond-the-prompt-building-deterministic-guardrails-for-genai-in-clinical-workflows/#respond</comments>
		
		<dc:creator><![CDATA[superadmin]]></dc:creator>
		<pubDate>Thu, 15 Oct 2026 07:35:54 +0000</pubDate>
				<category><![CDATA[Blog]]></category>
		<category><![CDATA[#Healthcare Generative AI]]></category>
		<category><![CDATA[FHIR RAG pipeline]]></category>
		<category><![CDATA[Healthcare software engineering.]]></category>
		<category><![CDATA[HIPAA compliant AI architecture]]></category>
		<category><![CDATA[PHI de-identification pipeline]]></category>
		<guid isPermaLink="false">https://santeware.com/?p=33447</guid>

					<description><![CDATA[The healthcare industry has officially moved past the initial awe of Large Language Models (LLMs). We know generative AI can draft clinical discharge summaries in seconds, summarize thousands of pages of longitudinal medical records, and synthesize lab results with impressive fluency. The Core Engineering Challenge: How do we introduce a non-deterministic, probabilistic engine into an [&#8230;]]]></description>
		
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