Retrieval Augmented Generation (RAG) is a common pattern used in generative AI solutions to ground prompts with your data. Azure AI Foundry provides support for adding data, creating indexes, and integrating them with generative AI models to help you build RAG-based solutions.
Learning objectives
By the end of this module, you'll be able to:
Identify the need to ground your language model with Retrieval Augmented Generation (RAG)
Index your data with Azure AI Search to make it searchable for language models
Build an agent using RAG on your own data in the Azure AI Foundry portal
If you already get the RAG concept, Azure AI Foundry mostly saves you wiring work, especially around indexing with Azure AI Search and keeping auth and embeddings consistent. In practice the hard part is still data prep and deciding what actually goes into the index, bad chunks or outdated docs will kill answer quality no matter the tooling. I have seen similar setups outside Azure too, for example lightweight RAG chatbots built on private docs where the value was in clean sources, not the platform itself, botino.eu works in a comparable way when the data is well curated. If you treat Foundry as an ops layer and not magic, it makes more sense.
