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 →Author: Nithin Mohan TK
Hello Namaste! 👋🏻
I'm an accomplished Enterprise Solution Architect & Engineering Leader with 20+ years of progressive experience architecting, building, and scaling transformative enterprise solutions.
⚜️ Solution Architect / AI & Cloud 📌 Galway, Ireland
Embedding Model Selection: Choosing the Right Model for Your RAG System
Introduction: Choosing the right embedding model is critical for RAG systems, semantic search, and similarity applications. The wrong choice leads to poor retrieval quality, high costs, or unacceptable latency. OpenAI’s text-embedding-3-small is cheap and fast but may miss nuanced similarities. Cohere’s embed-v3 excels at multilingual content. Open-source models like BGE and E5 offer privacy and […]
Read more →Hybrid Search Strategies: Combining Keyword and Semantic Search for Superior Retrieval
Introduction: Neither keyword search nor semantic search is perfect alone. Keyword search excels at exact matches and specific terms but misses semantic relationships. Semantic search understands meaning but can miss exact phrases and rare terms. Hybrid search combines both approaches, leveraging the strengths of each to deliver superior retrieval quality. This guide covers practical hybrid […]
Read more →Semantic Search Optimization: Building High-Quality Retrieval Systems
Introduction: Semantic search goes beyond keyword matching to understand the meaning and intent behind queries. By converting text to dense vector embeddings, semantic search finds conceptually similar content even when exact words don’t match. However, naive implementations often underperform—poor embedding choices, suboptimal indexing, and lack of reranking lead to irrelevant results. This guide covers practical […]
Read more →Document Chunking Strategies: Optimizing RAG Retrieval Quality
Introduction: RAG systems live or die by their chunking strategy. Chunk too large and you waste context window space with irrelevant content. Chunk too small and you lose semantic coherence, making it hard for the LLM to understand context. The right chunking strategy depends on your document types, query patterns, and retrieval approach. This guide […]
Read more →Embedding Fine-Tuning: Training Custom Embeddings for Domain-Specific Retrieval
Introduction: Off-the-shelf embedding models work well for general text, but domain-specific applications often need better performance. Fine-tuning embeddings on your data can dramatically improve retrieval quality—turning a 70% recall into 90%+ for your specific use case. The key is creating high-quality training data that teaches the model what “similar” means in your domain. This guide […]
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