Introduction: Fine-tuning transforms a general-purpose LLM into a specialized model tailored to your domain, style, or task. While prompt engineering can get you far, fine-tuning offers consistent behavior, reduced token usage, and capabilities that prompting alone cannot achieve. This guide covers the complete fine-tuning workflow—from data preparation to deployment—using both cloud APIs (OpenAI, Together AI) […]
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Hugging Face Transformers: The Complete Guide to Open-Source AI Model Deployment
Introduction: Hugging Face Transformers has become the de facto standard library for working with transformer-based models. With access to over 500,000 pre-trained models and 150,000 datasets through the Hugging Face Hub, it provides the most comprehensive ecosystem for deploying open-source AI models. Whether you’re running Llama, Mistral, or fine-tuning your own models, Transformers offers a […]
Read more →Model Routing Strategies: Intelligent Request Distribution Across LLMs
Introduction: Not every request needs GPT-4. Simple questions can be handled by smaller, faster, cheaper models, while complex reasoning tasks benefit from more capable ones. Model routing intelligently directs requests to the most appropriate model based on task complexity, cost constraints, latency requirements, and quality needs. This approach can reduce costs by 50-80% while maintaining […]
Read more →FHIR Subscriptions: Building Real-Time Event-Driven Healthcare Apps
🏥 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 Guide EMR Modernization: Migrating from Legacy HL7 v2 to FHIR HL7 v3: Understanding RIM and Why v3 Failed to Replace v2 […]
Read more →Embedding Models Deep Dive: From Sentence Transformers to Production Deployment
Introduction: Embeddings are the foundation of modern AI applications—they transform text, images, and other data into dense vectors that capture semantic meaning. Understanding how embedding models work, their strengths and limitations, and how to choose between them is essential for building effective search, RAG, and similarity systems. This guide covers the landscape of embedding models: […]
Read more →Google Agent Development Kit (ADK): Building Your First AI Agent – Part 1 of 5
Learn how to build production-ready AI agents with Google Agent Development Kit (ADK). This comprehensive tutorial covers architecture fundamentals, setup, and your first search assistant agent with C4 diagrams, code examples, and deployment strategies.
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