Executive Summary FHIR (Fast Healthcare Interoperability Resources) has become the de facto standard for healthcare data exchange. This article shares production-tested best practices from implementing FHIR integrations across multiple EMR systems, processing 50M+ API calls monthly. 🏥 HEALTHCARE INTEROPERABILITY SERIES This article is part of a comprehensive series on healthcare data standards and interoperability. HL7 […]
Read more →Month: July 2025
Security as Code: Why DevSecOps Is No Longer Optional in 2025
The traditional approach to security—treating it as a final checkpoint before deployment—has become a liability in modern software delivery. After two decades of building enterprise systems, I’ve witnessed the painful evolution from “security as an afterthought” to “security as code.” In 2025, DevSecOps isn’t just a best practice; it’s a survival requirement for any organization […]
Read more →Multi-Modal LLM Integration: Building Applications with Vision Capabilities
Introduction: Modern LLMs understand more than text. GPT-4V, Claude 3, and Gemini can process images alongside text, enabling applications that reason across modalities. Building multi-modal applications requires handling image encoding, managing mixed-content prompts, and designing interactions that leverage visual understanding. This guide covers practical patterns for integrating vision capabilities: encoding images for API calls, building […]
Read more →LLM Rate Limiting and Throttling: Building Resilient AI Applications
Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request caps. Hit these limits and your application grinds to a halt with 429 errors. Worse, aggressive retry logic can trigger longer cooldowns. Proper rate limiting isn’t just about staying under limits; it’s about maximizing throughput while gracefully handling bursts, prioritizing […]
Read more →Privacy-Preserving AI: Techniques for Sensitive Data
Last year, we trained a model on customer data. A researcher showed they could reconstruct customer information from model outputs. After implementing privacy-preserving techniques across 10+ projects, I’ve learned how to protect sensitive data while enabling AI capabilities. Here’s the complete guide to privacy-preserving AI. Figure 1: Privacy-Preserving AI Techniques Overview Why Privacy-Preserving AI Matters: […]
Read more →LLM Request Batching: Maximizing Throughput with Parallel Processing
Introduction: Processing LLM requests one at a time is inefficient. When you have multiple independent requests, sequential processing wastes time waiting for each response before starting the next. Batching groups requests together for parallel processing, dramatically improving throughput. But batching LLM requests isn’t straightforward—you need to handle rate limits, manage concurrent connections, deal with partial […]
Read more →