Azure-Openai

The OWASP LLM Top 10 exists because shipping an LLM to production without a security model is a new category of risk that the existing web application security playbook doesn’t fully cover. Prompt injection has held the #1 spot on that list since the first version was published, and it’s not there because researchers think it might be a problem someday. It’s been demonstrated against production systems at companies that knew what they were doing.

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.

macOS has had built-in dictation since Monterey. It is fine — press and hold a key, speak, done. But it requires Apple’s servers (unless you download the enhanced on-device model), only works in some apps, and you have zero control over punctuation, formatting, or hotkeys.

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.