Compare LangChain, LlamaIndex, Semantic Kernel, and more. Learn when to use each framework and build production-ready RAG systems with practical code examples.
Read more →FHIR API Security Part 2: Implementation & Best Practices
Executive Summary Part 2 of 2: Implementation & Best Practices 🏥 HEALTHCARE INTEROPERABILITY SERIES This article is part of a comprehensive series on healthcare data standards and interoperability. HL7 v2: The Messaging Standard That Powers Healthcare IT Building GDPR-Compliant FHIR APIs: A European Healthcare … EMR Modernization: Migrating from Legacy HL7 v2 to FHIR HL7 […]
Read more →The Python Renaissance: Why 2025 Is the Year Everything Changed for Data Engineers
🎓 AUTHORITY NOTE This analysis draws from 20+ years of Python experience in enterprise data engineering, covering production deployments at scale across multiple Fortune 500 companies. Executive Summary Something remarkable happened in the Python ecosystem over the past year. After decades of incremental improvements, we’ve witnessed a fundamental shift in how data engineers approach their […]
Read more →Building AI-Powered Frontends: Real-Time LLM Interactions in React
Building AI-Powered Frontends: Real-Time LLM Interactions in React Expert Guide to Creating Seamless, Real-Time AI Experiences in Modern React Applications After building dozens of AI-powered applications over the past few years, I’ve learned that the frontend experience makes or breaks an AI product. It’s not enough to have a powerful LLM backend—users need to feel […]
Read more →Retrieval Augmented Fine-Tuning (RAFT): Training LLMs to Excel at RAG Tasks
Introduction: Retrieval Augmented Fine-Tuning (RAFT) represents a powerful approach to improving LLM performance on domain-specific tasks by combining the benefits of fine-tuning with retrieval-augmented generation. Traditional RAG systems retrieve relevant documents at inference time and include them in the prompt, but the base model wasn’t trained to effectively use retrieved context. RAFT addresses this by […]
Read more →Retrieval Evaluation Metrics: Measuring What Matters in Search and RAG Systems
Introduction: Retrieval evaluation is the foundation of building effective RAG systems and search applications. Without proper metrics, you’re flying blind—unable to tell if your retrieval improvements actually help or hurt end-user experience. This guide covers the essential metrics for evaluating retrieval systems: precision and recall at various cutoffs, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative […]
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