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...

IChatClient in .NET: A Practical Guide for Building AI Applications If you are building AI features in a .NET application, getting your first response from a large language model is usually the easy part. The harder problem appears when the application grows. Provider-specific SDKs start appearing inside application services, prompts become coupled to infrastructure code, conversation history needs to be managed, and concerns such as logging, telemetry, caching, rate limiting, security, and cost tracking begin spreading across the codebase. This is where IChatClient becomes useful. IChatClient is part of the Microsoft.Extensions.AI ecosystem. It provides a common .NET abstraction for interacting with chat-capable AI services while allowing the underlying implementation to come from a provider SDK or another compatible implementation. The architectural idea is simple: your application can depend on an AI capabilit...