Update retrieval-augmented-generation-overview.md

Update wording to change from "Retrieval Augmentation Generation" to "Retrieval Augmented Generation".

Author: DanWahlin

articles/search/retrieval-augmented-generation-overview.md

@@ -15,7 +15,7 @@ ms.date: 11/20/2023
 
 # Retrieval Augmented Generation (RAG) in Azure AI Search
 
-Retrieval Augmentation Generation (RAG) is an architecture that augments the capabilities of a Large Language Model (LLM) like ChatGPT by adding an information retrieval system that provides grounding data. Adding an information retrieval system gives you control over grounding data used by an LLM when it formulates a response. For an enterprise solution, RAG architecture means that you can constrain generative AI to *your enterprise content* sourced from vectorized documents and images, and other data formats if you have embedding models for that content.
+Retrieval Augmented Generation (RAG) is an architecture that augments the capabilities of a Large Language Model (LLM) like ChatGPT by adding an information retrieval system that provides grounding data. Adding an information retrieval system gives you control over grounding data used by an LLM when it formulates a response. For an enterprise solution, RAG architecture means that you can constrain generative AI to *your enterprise content* sourced from vectorized documents and images, and other data formats if you have embedding models for that content.
 
 The decision about which information retrieval system to use is critical because it determines the inputs to the LLM. The information retrieval system should provide: