AI & MLBeginner
AI in .NET: The Complete Landscape for Developers
Map the 2026 AI in .NET ecosystem: Microsoft.Extensions.AI, Agent Framework, Semantic Kernel, vector data, MCP, ML.NET, ONNX and Foundry, and when to use each.
RAG & Vector Search
7 articles about RAG & Vector Search: in-depth .NET and AI guides, senior interview questions and AI news on DotNet AI Hub.
Technologies 4
AI & MLAdvanced
Retrieval-Augmented Generation (RAG) in .NET: End-to-End Guide
Build production RAG in C#, covering ingestion, chunking, embeddings, hybrid retrieval, reranking, grounded prompts, citations and evaluation in .NET.
AI & MLIntermediate
Embeddings and Vector Databases in .NET: A Practical Guide
Learn how embeddings and vector databases work in .NET: similarity metrics, HNSW indexes, Microsoft.Extensions.VectorData and pgvector, SQL Server and Qdrant.
Data & EF CoreIntermediate
PostgreSQL with .NET: Npgsql, EF Core and pgvector
A practical guide to PostgreSQL in .NET: Npgsql pooling and multiplexing, EF Core JSONB, arrays, full-text search, COPY imports and pgvector search.
Interview Questions 2
AI EngineeringArchitect
RAG and Vector Search Interview Questions
Architect-level interview questions on RAG: chunking, embedding choice, hybrid search, reranking, groundedness, stale data, permissions and cost control.
System DesignArchitect
System Design Interview: Enterprise AI Assistant (RAG Platform)
Architect-level system design questions on an enterprise RAG platform in .NET: ingestion, retrieval, security trimming, caching and cost control.
AI News 1
Microsoft.Extensions.AI and Vector Data Extensions Reach General Availability
Microsoft declared Microsoft.Extensions.AI and the Vector Data extensions generally available in May 2025, giving .NET a stable, provider-neutral AI layer.