LLMs, RAG, vector search, Azure OpenAI, and intelligent agents for .NET teams.
6 posts · 8 questions
LLMs from first principles: tokenization, embeddings, RoPE, self-attention, multi-head, nanoGPT, pretraining, SFT, LoRA, QLoRA, DPO, vLLM serving.
The MLOps playbook: deploying models (REST/batch/streaming), versioning + registry + CI/CD, drift monitoring, cloud strategies, and a full end-to-end app.
How Mattrx swapped its Python ML service for ML.NET — regression, classification, and clustering trained in C#, in-process, no PhD required.
Full C# RAG chatbot: Semantic Kernel + Azure AI Search hybrid + reranker + streaming + tenant security. Mattrx Help: 4% hallucination, 91% recall.
Agentic vs Traditional RAG in .NET — when each wins, full Semantic Kernel code, the router that saved Mattrx $10,900/month on AI bills.
Prompt injection, data leakage, tenant breaches — seven real attacks on enterprise LLM apps, and the Mattrx production code that stops each one.