Executive Summary HL7 v3 was designed in the 1990s as the successor to HL7 v2, promising a rigorous, model-driven approach based on the Reference Information Model (RIM). Despite 20+ years of development and standardization, v3 never achieved widespread adoption. Understanding why v3 failed—and where it still matters—is crucial for architects navigating healthcare interoperability standards. 🏥 […]
Read more →Search Results for: title
Tool Use and Function Calling: Extending LLM Capabilities with External Actions
Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just producing text responses, models can now decide when to call external functions, APIs, or tools to accomplish tasks. This capability enables building assistants that can search the web, query databases, send emails, execute code, and interact with any system that exposes […]
Read more →CDA (Clinical Document Architecture): The XML Standard for Medical Documents
What is CDA and Why It Matters CDA Document Structure Sample CDA Document Structure .NET CDA Parsing Implementation CDA Document Generation Common CDA Sections (C-CDA) CDA vs FHIR Documents Standards and References Related Articles in This Series Conclusion
Read more →Enterprise Observability on Google Cloud: Mastering Logging, Monitoring, and Distributed Tracing
Introduction: Google Cloud’s operations suite (formerly Stackdriver) provides comprehensive observability through Cloud Logging, Cloud Monitoring, Cloud Trace, and Error Reporting. This guide explores enterprise observability patterns, from log aggregation and custom metrics to distributed tracing and intelligent alerting. After implementing observability platforms for organizations running thousands of microservices, I’ve found GCP’s integrated approach delivers exceptional […]
Read more →Structured Output from LLMs: Instructor Library and Production Patterns (Part 2 of 2)
Introduction: Getting LLMs to return structured data instead of free-form text is essential for building reliable applications. Whether you need JSON for API responses, typed objects for downstream processing, or specific formats for data extraction, structured output techniques ensure consistency and parseability. This guide covers the major approaches: JSON mode, function calling, the Instructor library, […]
Read more →Streaming LLM Responses: Building Real-Time AI Applications (Part 2 of 2)
Introduction: Waiting 10-30 seconds for an LLM response feels like an eternity. Streaming changes everything—users see tokens appear in real-time, creating the illusion of instant response even when generation takes just as long. Beyond UX, streaming enables early termination (stop generating when you have enough), progressive processing (start working with partial responses), and better error […]
Read more →