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Deep Dive: Implementing Retrieval-Augmented Generation (RAG) with Azure AI Search
Artificial Intelligence has changed the way businesses handle information, automate tasks, and interact with users. Large Language Models (LLMs) such as GPT-based systems can generate impressive responses, but they have one major limitation: they do not automatically know your private business data, internal documents, or the latest information.
https://dellenny.com/deep-dive-implementing-retrieval-augmented-generation-rag-with-azure-ai-search/
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Nice topic. One point I would emphasize for Azure AI Search RAG implementations is that retrieval quality matters more than the prompt once the basic app is working.
The highest-impact checks I usually recommend are: use hybrid retrieval instead of vector-only search, enable semantic ranking where it fits the workload, keep chunk sizes tied to the document structure, store source metadata for citations, and build a small evaluation set of real user questions before tuning prompts.
It is also worth testing failure cases: outdated documents, duplicate chunks, conflicting source material, and questions where the correct answer is not in the index. Those tests quickly show whether the app is grounded or just producing fluent guesses.
Microsoft's RAG overview is a useful companion: https://learn.microsoft.com/azure/search/retrieval-augmented-generation-overview
