Flagship hands-on RAG course

Building Production RAG Systems using Azure AI Search

A practical workshop for teams that need retrieval quality, grounded answers, citation discipline, security controls and operational reliability from their enterprise RAG systems.

Retrieval-augmented generation is easy to prototype and hard to operate. A demo that answers questions from a small PDF set becomes a different beast when it must serve real documents at scale: chunking decisions that silently break retrieval, embeddings that drift from the corpus, answers that sound confident but are not grounded, citations that point at the wrong source, and no way to tell whether the system is getting better or worse after deployment. This workshop exists to close that gap — turning RAG from a weekend proof of concept into a measured, auditable production capability.

Over two days you work through the full RAG lifecycle on Azure. You start with fundamentals — when retrieval beats fine-tuning, how grounding and citations work — then build the data ingestion pipeline: parsing source systems, chunking with the right sizes and metadata, and a refresh strategy that keeps indexes current. You configure Azure AI Search with analyzers, vector and hybrid search, scoring and filters; choose embeddings and reranking for measurable retrieval quality; and design the application layer with prompt assembly, citations, fallback behaviour, authorization and sensible UX boundaries. The course ends where production begins: evaluation with golden datasets, relevance tests, monitoring, cost control and data freshness — the discipline that lets you prove the system works and keep it working.

You leave with a RAG system that has been designed, built and evaluated — not just discussed — plus the evaluation datasets, monitoring templates and operational runbooks to keep it honest after the workshop ends. The course suits developers, data engineers and AI teams who are already experimenting with RAG and want to move it into production responsibly, and the technical leads who will own retrieval quality and answer for the system's behaviour. By the final day you will be able to articulate why a RAG answer is (or is not) trustworthy, prove it with relevance metrics, and defend the architecture choices — chunking, embeddings, search and evaluation — to your stakeholders.