Building Production RAG Systems in .NET 10: The Complete Guide to Embeddings

Building Production RAG Systems in .NET 10: The Complete Guide to Embeddings The Hallucination Problem Your company spent $50K building an internal chatbot. It tells customers "yes, we ship internationally" when you only ship to the US. Your support team is drowning in corrections. Sound familiar? This happens because traditional LLMs generate responses from training data patterns, not your actual data. They hallucinate. They confidently state false information. RAG (Retrieval-Augmented Generation) fixes this. Instead of hoping the LLM knows about your data, you explicitly feed it your documents first. What Are Embeddings? Think of embeddings as a way to convert text into mathematics. The Simple Version Text: "The quick brown fox" ↓ Embedding (float array, 1536 dimensions) [0.234, -0.156, 0.892, ..., 0.421] ↓ This vector captures semantic meaning Why Vectors Matter Two sentences with different words can have similar embeddings if they mean the same thing: Sentence A: "Our Q3 revenue ex