🏥 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 […]
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Conversation Memory Patterns: Building Stateful LLM Applications
Introduction: LLMs are stateless—each request starts fresh with no memory of previous interactions. Building conversational applications requires implementing memory systems that maintain context across turns while staying within token limits. The challenge is balancing completeness (keeping all relevant context) with efficiency (not wasting tokens on irrelevant history). This guide covers practical memory patterns: buffer memory […]
Read more →ADK Building Blocks: Tools, Memory, and State Management – Part 2 of 5
Deep dive into ADK building blocks: custom tools, memory patterns, and state management. Learn to build production-ready agents with database integration, conversation memory, and intelligent caching.
Read more →Embedding Dimensionality Reduction: Compressing Vectors Without Losing Semantics
Introduction: High-dimensional embeddings from models like OpenAI’s text-embedding-3-large (3072 dimensions) or Cohere’s embed-v3 (1024 dimensions) deliver excellent semantic understanding but come with costs: more storage, slower similarity computations, and higher memory usage. For many applications, you can reduce dimensions significantly while preserving most of the semantic information. This guide covers practical dimensionality reduction techniques: PCA […]
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 →Embedding Space Analysis: Visualizing and Understanding Vector Representations
Introduction: Understanding embedding spaces is crucial for building effective semantic search, RAG systems, and recommendation engines. Embeddings map text, images, or other data into high-dimensional vector spaces where similar items cluster together. But how do you know if your embeddings are working well? How do you debug retrieval failures or understand why certain queries return […]
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