Transitioning to AI: Engineering the Next Paradigm

LLM Evaluation in .NET: Metrics, RAG, Agents and AI Testing Building an application with a large language model is relatively easy. Making sure that application continues to produce useful, accurate, reliable, safe, and affordable results is much harder. An LLM can generate a response in a few seconds, but the fact that it generated an answer does not tell us whether the answer was good. Was it correct? Did it actually answer the user's question? If the application uses RAG, did the answer come from the retrieved information? If an AI agent was involved, did it select the correct tool? Did the tool receive valid arguments? There are also operational questions: How many tokens were consumed? How much did the request cost? How long did the user wait? Did the system use unnecessary tools or model calls? Did anything get worse after a prompt, model, or retrieval change? These questions make LLM evaluation an important part of production AI engineering. Tradition...

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 ...