LLM integration & RAG Courses

The real value for developers comes when the models are integrated directly into your own systems through APIs from OpenAI, Azure or Hugging Face. You learn to call LLM APIs from Python, work with structured output and function calling, handle errors, costs and rate limits, and build solutions such as chatbots, writing assistants and automated workflows. You also gain insight into the design choices that separate a demo from a robust solution: prompt templates, systematic evaluation of responses and protection against prompt injection.

A language model only knows what it was trained on, and nothing about your internal documents, systems or latest figures. RAG solves that: documents are split into chunks and made searchable with embeddings in a vector database or with vector search directly in SQL Server, Azure SQL and Microsoft Fabric; the most relevant excerpts are retrieved for each question and passed to the model as context; the answer is grounded in your own knowledge and can point back to the sources. You learn the whole chain, from chunking and embeddings through search to the prompt that ties it all together, and the choices that determine quality: how large the chunks are, which embedding model to use, how many sources to include.

Real-world solutions are rarely a single call. Frameworks such as LangChain let you build workflows that chain together multiple steps, models and data sources; function calling and structured output let the model call your own functions and return data in a fixed format; and in Azure AI Foundry, prompt flows bring the same patterns together in a managed platform with built-in language and speech services (Azure AI Language and Speech). You learn how to choose between writing the orchestration yourself and using a framework or a platform, and where the line is drawn with true AI agents that plan their own actions.

The difference between a demo and a solution in production is evaluation. You learn to measure answer quality systematically instead of relying on gut feeling, to test for hallucinations and for prompt injection, where malicious input tries to hijack the model, to keep secrets and personal data out of prompts and logs, and to keep track of costs and rate limits. Responsible AI is part of the same craft: filters for harmful content, traceability in the answers, and clear limits on what the solution may be used for.

Some tasks require that data never leaves the company. Open-source models from Hugging Face can run locally or in your own environment with tools such as LM Studio, so sensitive data is never sent to an external service. You get hands-on experience downloading, running and evaluating local models and choosing a model based on the hardware you have, along with a foundation for choosing between cloud-based and local solutions based on requirements for data security, quality and cost.