Transitioning to AI: Engineering the Next Paradigm

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...

AI Engineering with .NET: A Practical Guide If you write C# for a living, there is a good chance someone has already asked you to "add AI" to an application. Maybe the requirement is document summarization, answering questions over company policies, classifying support tickets, generating content, extracting structured data, or building a chatbot. The interesting part is that most of these problems do not require you to become a machine-learning researcher. They require you to understand how AI models fit into a software system: how to call them, structure their output, provide application context, handle failures, control cost, evaluate responses, and keep the system observable in production. That is where AI engineering with .NET becomes useful. If you already work with C#, ASP.NET Core, dependency injection, APIs, databases, background services, logging, and distributed systems, you already have much of the foundation requir...