changed proven-solution link

Author: HeidiSteen

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

@@ -10,12 +10,12 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 11/17/2023
+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 the data. Adding an information retrieval system gives you control over the 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, images, audio, and video.
+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, images, audio, and video.
 
 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:
 
@@ -27,7 +27,7 @@ The decision about which information retrieval system to use is critical because
 
 + Integration with LLMs.
 
-Azure AI Search is a [proven solution for information retrieval](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) in a RAG architecture. It provides indexing and query capabilities, with the infrastructure and security of the Azure cloud. Through code and other components, you can design a comprehensive RAG solution that includes all of the elements for generative AI over your proprietary content. 
+Azure AI Search is a [proven solution for information retrieval](/azure/developer/python/get-started-app-chat-template?tabs=github-codespaces) in a RAG architecture. It provides indexing and query capabilities, with the infrastructure and security of the Azure cloud. Through code and other components, you can design a comprehensive RAG solution that includes all of the elements for generative AI over your proprietary content. 
 
 > [!NOTE]
 > New to copilot and RAG concepts? Watch [Vector search and state of the art retrieval for Generative AI apps](https://ignite.microsoft.com/sessions/18618ca9-0e4d-4f9d-9a28-0bc3ef5cf54e?source=sessions).
@@ -96,7 +96,7 @@ Since you probably know what kind of content you want to search over, consider t
 | video | vectors <sup>1</sup> | Video files can be vectorized externally for a mathematical representation of video content and then [indexed as vector fields](vector-search-how-to-create-index.md) in your index. |
 | audio | vectors <sup>1</sup> | Audio files can be vectorized externally for a mathematical representation of audio content and then [indexed as vector fields](vector-search-how-to-create-index.md) in your index. |
 
- <sup>1</sup> [Vector support](vector-search-overview.md) is in public preview. It currently requires that you call other libraries or models for data chunking and vectorization. See [this repo](https://github.com/Azure/cognitive-search-vector-pr) for samples that call Azure OpenAI embedding models to vectorize content and queries, and that demonstrate data chunking.
+ <sup>1</sup> The generally available functionality of [vector support](vector-search-overview.md) requires that you call other libraries or models for data chunking and vectorization. However, [integrated vectorization (preview)](vector-search-integrated-vectorization.md) embeds these steps. For code samples showing both approaches, see [azure-search-vectors repo](https://github.com/Azure/azure-search-vector-samples).
 
 <sup>2</sup> [Skills](cognitive-search-working-with-skillsets.md) are built-in support for [AI enrichment](cognitive-search-concept-intro.md). For OCR and Image Analysis, the indexing pipeline makes an internal call to the Azure AI Vision APIs. These skills pass an extracted image to Azure AI for processing, and receive the output as text that's indexed by Azure AI Search.