Markus Egger argues that applications without AI Copilot features are now legacy, declaring a paradigm shift comparable to the transitions from command-line to Windows, desktop to web, and web to mobile. The article explains how Azure OpenAI services enable developers to integrate large language models (LLMs) into their own applications, starting with simple completions and chat implementations in both Python and C#. Egger walks through setting up Azure OpenAI resources, deploying models like GPT-3.5, and building a streaming chatbot with custom system prompts.

The core of the article focuses on the Retrieval Augmented Generation (RAG) pattern — a technique for grounding AI responses in your own proprietary data rather than relying solely on the LLM's training data. Egger uses a vacation rental business as an example, demonstrating how to detect user intent, generate search terms using few-shot prompt engineering, retrieve relevant documents from Azure Cognitive Search, and augment prompts with that data to produce accurate, context-specific answers. He also covers practical considerations like token limits, model selection tradeoffs between cost and capability, temperature settings for predictability, and the pros and cons of streaming responses versus verifying output before displaying it. The article positions itself as a starting point, promising deeper explorations of search indexing, SQL Server integration, and advanced intent detection in future installments.