Transitioning to AI: Engineering the Next Paradigm

Vector Databases in .NET: RAG, pgvector, HNSW and Production Design If you are building AI features in a .NET application, calling an LLM is often the easy part. The harder problem starts when the model needs information that is specific to your application. That information might be stored in an internal policy document, product catalog, support ticket, technical manual, compliance document, source repository, or company knowledge base. An LLM does not automatically know how to search those systems simply because your application can access them. Your application needs a retrieval layer that can identify useful information and provide it to the model. This is where embeddings, vector search, and vector databases become useful. A vector database stores numerical representations of information called embeddings and provides mechanisms for retrieving records according to vector similarity. This makes vector search an important building block for RAG applications, semantic ...

AI Agents in .NET: Tools, RAG, MCP and Workflows A practical guide to AI agent architecture for .NET developers, covering tool calling, RAG, MCP, workflows, security, testing, observability, and production design. If you write software for a living, you've probably already used an AI model. The more interesting question is what happens when the model needs to do something instead of simply answer something. Maybe it needs to search a knowledge base, inspect an order, call an API, create a support ticket, retrieve information from a database, request approval, or determine which step should happen next. That's where AI agents become useful. An agent is not simply a chatbot with a new label. An agent combines a model with instructions, context, tools, state, and an execution loop so that an application can work toward an objective. For .NET developers, this is particularly interesting because much of the difficult work still looks ...

Understanding Embeddings in RAG with .NET This article focuses on the practical engineering side of embeddings, semantic retrieval, and RAG applications built with .NET. If you've started building Retrieval-Augmented Generation (RAG) applications, you've probably heard the word embedding more times than you expected. Documents become embeddings. Questions become embeddings. A vector database stores those embeddings, and a similarity search retrieves vectors that are close to the vector representing the user's question. The workflow looks simple when drawn as a diagram. The more interesting engineering question is what happens between the text and the vector: How can a list of numbers represent the meaning of a sentence? That is the question this article focuses on. Rather than treating embeddings as another AI bu...