Addressed feedback

Author: haileytap

articles/search/includes/quickstarts/agentic-retrieval-python.md

@@ -9,9 +9,9 @@ ms.date: 09/05/2025
 
 [!INCLUDE [Feature preview](../previews/preview-generic.md)]
 
-In this quickstart, you use [agentic retrieval](../../search-agentic-retrieval-concept.md) to create a conversational search experience powered by documents indexed in Azure AI Search and large language models (LLMs) from Azure AI Foundry Models.
+In this quickstart, you use [agentic retrieval](../../search-agentic-retrieval-concept.md) to create a conversational search experience powered by documents indexed in Azure AI Search and large language models (LLMs) from Azure OpenAI in Azure AI Foundry Models.
 
-A *knowledge agent* orchestrates agentic retrieval by decomposing complex queries into subqueries, running the subqueries against one or more *knowledge sources*, and returning results with metadata. By default, the agent outputs raw content from your sources, but this quickstart passes the output to an LLM for natural-language answer generation.
+A *knowledge agent* orchestrates agentic retrieval by decomposing complex queries into subqueries, running the subqueries against one or more *knowledge sources*, and returning results with metadata. By default, the agent outputs raw content from your sources, but this quickstart uses the answer synthesis modality for natural-language answer generation.
 
 Although you can provide your own data, this quickstart uses [sample JSON documents](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/nasa-e-book/earth-at-night-json) from NASA's Earth at Night e-book. The documents describe general science topics and images of Earth at night as observed from space.
 

articles/search/includes/quickstarts/agentic-retrieval-rest.md

@@ -9,9 +9,9 @@ ms.date: 08/26/2025
 
 [!INCLUDE [Feature preview](../previews/preview-generic.md)]
 
-In this quickstart, you use [agentic retrieval](../../search-agentic-retrieval-concept.md) to create a conversational search experience powered by documents indexed in Azure AI Search and large language models (LLMs) from Azure AI Foundry Models.
+In this quickstart, you use [agentic retrieval](../../search-agentic-retrieval-concept.md) to create a conversational search experience powered by documents indexed in Azure AI Search and large language models (LLMs) from Azure OpenAI in Azure AI Foundry Models.
 
-A *knowledge agent* orchestrates agentic retrieval by decomposing complex queries into subqueries, running the subqueries against one or more *knowledge sources*, and returning results with metadata. By default, the agent outputs raw content from your sources, but this quickstart passes the output to an LLM for natural-language answer generation.
+A *knowledge agent* orchestrates agentic retrieval by decomposing complex queries into subqueries, running the subqueries against one or more *knowledge sources*, and returning results with metadata. By default, the agent outputs raw content from your sources, but this quickstart uses the answer synthesis modality for natural-language answer generation.
 
 Although you can provide your own data, this quickstart uses [sample JSON documents](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/nasa-e-book/earth-at-night-json) from NASA's Earth at Night e-book. The documents describe general science topics and images of Earth at night as observed from space.