Entity Framework Core 10, released alongside .NET 10, introduces features that position it as a first-class choice for AI-powered applications. The headline addition—vector search support—enables semantic similarity queries directly in LINQ, while new LeftJoin/RightJoin operators and Cosmos DB full-text search round out a release focused on modern data access patterns. This comprehensive guide explores each […]
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Getting Started with Full Stack AI Engineering: A Practical Guide for 2026
A comprehensive guide to becoming a Full Stack AI Engineer in 2026. Learn the complete stack from frontend to infrastructure, with practical code examples using GPT-5, Python, FastAPI, LangChain, and Next.js for building AI-powered applications.
Read more →Building Enterprise AI Applications with AWS Bedrock: What Two Years of Production Experience Taught Me
When AWS announced Bedrock in 2023, I was skeptical. Another managed AI service promising to simplify generative AI adoption? After two years of production deployments across financial services, healthcare, and retail, I’ve learned what actually matters when building enterprise AI applications. AWS Bedrock Enterprise Architecture The Foundation Model Landscape Has Matured The most significant evolution […]
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 →Advanced RAG Patterns: Query Rewriting and Self-Reflective Retrieval (Part 2 of 2)
Introduction: Basic RAG retrieves documents and stuffs them into context. Advanced RAG transforms retrieval into a sophisticated pipeline that dramatically improves answer quality. This guide covers the techniques that separate production RAG systems from prototypes: query rewriting to improve retrieval, hybrid search combining dense and sparse methods, cross-encoder reranking for precision, contextual compression to fit […]
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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