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RAG & Embeddings

Explore retrieval-augmented generation and embeddings in .NET, including vector search, semantic retrieval, and grounding techniques for C# applications.

Articles

• 08/24/2026
• • hybrid search RAG .NET
  • Azure AI Search
  • BM25
  • vector search
  • reciprocal rank fusion
• 9 minute read

Hybrid Search for RAG in .NET: BM25, Vectors, and Reciprocal Rank Fusion

Learn how hybrid search RAG .NET combines lexical BM25 and dense vector candidates in one Azure AI Search request with reciprocal rank fusion.

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• 08/20/2026
• • Azure AI Search
  • vector search
  • .NET
  • C#
  • RAG
• 9 minute read

Vector Search in .NET: The Azure AI Search Query Model

Learn Azure AI Search vector search .NET query mechanics, including vector fields, dimension alignment, top-k results, filters, scores, and stable C# APIs.

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• 08/16/2026
• • Microsoft.Extensions.AI
  • embeddings
  • .NET
  • C#
  • RAG
• 9 minute read

Embedding Pipelines in .NET with Microsoft.Extensions.AI

Learn how Microsoft.Extensions.AI embeddings .NET pipelines use batching, vectors, caching, telemetry, and decorators to keep retrieval work observable.

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• 08/12/2026
• • RAG
  • embeddings
  • .NET
  • C#
  • retrieval-augmented generation
• 10 minute read

RAG and Embeddings in .NET: A Stable Architecture Guide

Learn a stable architecture for RAG and embeddings in .NET, connecting ingestion, retrieval, citations, authorization, evaluation, observability, and deletion.

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