Building and Evaluating RAG Solutions with Azure AI Search

Most generative AI answers are wrong, not because the model is weak, but because the retrieval underneath it is.
In this course, Building and Evaluating RAG Solutions with Azure AI Search, you'll gain the ability to design, build, and evaluate the retrieval layer of a production RAG solution.
First, you'll explore where Azure AI Search fits in a RAG architecture, and how full-text, vector, and hybrid search differ in what they actually return for the same question.
Next, you'll discover how to prepare organizational data for retrieval, designing an indexing pipeline with skill sets for chunking and OCR, configuring integrated vectorization so embeddings are generated and maintained for you, and applying metadata filters and semantic ranking to lift relevance.
Finally, you'll learn how to connect your index as a data source to a model in Microsoft Foundry, construct a grounded prompt pipeline, and measure the result using groundedness and relevance evaluators.
When you're finished with this course, you'll have the skills and knowledge of Azure AI Search needed to deliver a RAG solution that produces grounded, relevant answers you can verify.

Homepage

https://app.pluralsight.com/ilx/video-courses/azure-ai-search-rag-solutions-building-evaluating/course-overview

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