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I’m a hands-on Fractional CTO and technology advisor with over 25 years of experience…
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Adam Peled
Silent errors The most dangerous errors aren’t loud. They’re quiet mistakes that get reused. One incorrect note can propagate downstream for weeks. So we build and deploy AI powered ambient clinical intelligence in 8–12 weeks, with confidence cues, review prompts, and traceable edits. Imagine fewer silent errors because uncertainty is visible.
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Maitreyee Joshi
Excited to see Thinking Machines Lab’s Tinker make fine-tuning language models much easier by abstracting away the complexity into an API call! At Avon Health, we’ve already built systems that use prompt engineering across our AI modules: - Healthcare ontologies (ICD, CPT, SNOMED, LOINC, HCPCS): We feed in these vocabularies at runtime so our AI Scribe can suggest accurate codes directly from clinician notes. - Custom visit note templates (SOAP, discharge, etc.): We inject note structures into prompts so our AI Scribe always drafts documentation in our schema. - Database schema + SQL generation: We teach the model our schema at runtime so our AI Data Analyst can generate SQL queries and surface insights as charts, tables, and visualizations. These have already been huge wins for our clinicians and revenue teams. But as context windows fill up, we end up doing more pre-processing to fit the right data into every prompt. Fine-tuning gives us a way to bake in that knowledge directly, opening the door to greater determinism and reliability. That’s why we’re excited about tools like Tinker — they’ll let us lock in the knowledge we’ve been injecting at runtime and make our AI assistants even faster, safer, and more reliable.
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