Llm

Before MCP existed, adding tools to an AI application meant writing the same glue code over and over. You had OpenAI’s function calling syntax. Anthropic had tool use with a slightly different schema. LangChain abstracted over both, but now you depended on LangChain’s versioning decisions. Every new model provider meant rewriting your tool definitions. Every new tool meant re-registering it across every AI integration you maintained.

Every vendor selling software right now claims their product is “agentic.” I’ve seen chatbots with a system prompt called an agent. I’ve seen a scheduled Python script described as autonomous AI. I’ve also shipped actual agents to production — at an insurance company, handling FNOL triage, policy lookup, and claims routing. The gap between what gets marketed as an agent and what you’d actually build is significant.

TL;DR: This guide walks you through building a production-ready RAG system using FastAPI, ChromaDB, MinIO, and OpenAI. Learn document chunking, vector embeddings, hybrid search, and real-world deployment strategies. Introduction # As a .NET developer watching the AI landscape evolve, I found myself both excited and skeptical. When tools like Claude.ai and ChatGPT started offering out-of-the-box RAG solutions, I wanted to build my own system with full control over the implementation.

TL;DR # This article demonstrates how to build a REST API that converts natural language into SQL queries using multiple LLM providers (OpenAI, Azure OpenAI, Claude, and Gemini). The system dynamically selects the appropriate AI service based on configuration, executes the generated SQL against a database, and returns structured results. It includes a complete implementation with a service factory pattern, Docker setup, and example usage.

Introduction # Artificial Intelligence is transforming how we build applications, particularly in creating natural, conversational user experiences. This article guides you through building a full-stack AI chat application using .NET on the backend, Angular for the frontend, and Azure OpenAI for powerful language model capabilities, all connected through real-time SignalR communication.