Transitioning to AI: Engineering the Next Paradigm

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...

Retrieval-Augmented Generation in .NET: A Practical RAG Guide If you're building AI applications with .NET, there is a point where simply sending a prompt to an LLM stops being enough. Generating a summary is easy. Generating an answer from your company's documents, policies, product information, support knowledge, database records, or internal documentation is a different problem. The model does not automatically know your private data. And even when information is available somewhere in a model's training data, you generally do not want an application to depend on the model remembering it correctly or knowing the latest version of a business policy. This is where Retrieval-Augmented Generation (RAG) becomes useful. Instead of asking an LLM to answer entirely from its general knowledge, your application first retrieves relevant information from an external knowledge source and provides that information to the model as context. RAG = retrieve releva...