Merge branch 'main' into release-openai-realtime-webrtc

Author: v-alje

articles/ai-foundry/how-to/develop/get-started-projects-vs-code.md

@@ -7,7 +7,7 @@ ms.service: azure-ai-foundry
 content_well_notification: 
   - AI-contribution
 ms.topic: how-to
-ms.date: 04/21/2025
+ms.date: 04/28/2025
 ms.reviewer: erichen
 ms.author: johalexander
 author: ms-johnalex
@@ -122,7 +122,7 @@ When you open a project in the Azure AI Foundry Extension, that project is set a
 
 Switch your default project by following these steps:
 
-1. Right-click on your deployed model and select the **Switch Default Project in Azure Extension** option. 
+1. Right-click on the Azure AI Foundry Project and select the **Switch Default Project in Azure Extension** option. 
 
 1. In the top center, select the Azure AI Foundry Project you want to use from the list of available projects and press Enter.
 

articles/ai-foundry/how-to/develop/vs-code-agents.md

@@ -7,13 +7,13 @@ ms.service: azure-ai-foundry
 content_well_notification: 
   - AI-contribution
 ms.topic: how-to
-ms.date: 04/23/2025
+ms.date: 04/28/2025
 ms.reviewer: erichen
 ms.author: johalexander
 author: ms-johnalex
 ---
 
-# Work with Azure AI Agent Service in Visual Studio Code
+# Work with Azure AI Agent Service in Visual Studio Code (Preview)
 
 After you [get started with the AI Foundry the VS Code extension](./get-started-projects-vs-code.md), you can work with [Azure AI Agent Service](/azure/ai-services/agents/overview). Agents are "smart" microservices that:
 
@@ -25,6 +25,8 @@ Agents combine AI models with tools to access and interact with your data.
 
 Azure AI Foundry developers can stay productive by developing, testing, and deploying agents in the familiar and powerful environment of VS Code.
 
+[!INCLUDE [feature-preview](../../includes/feature-preview.md)]
+
 ###	Create and edit Azure AI Agents within the designer view
 
 Follow these steps to create an Azure AI Agent:

articles/ai-foundry/how-to/fine-tune-managed-compute.md

@@ -5,7 +5,7 @@ description: Learn how to fine-tune models using a managed compute with Azure AI
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: how-to
-ms.date: 11/22/2024
+ms.date: 04/25/2025
 ms.reviewer: vkann
 reviewer: kvijaykannan
 ms.author: mopeakande
@@ -77,4 +77,5 @@ In this section, you go through the steps to configure fine-tuning for your mode
 ## Related Contents
 
 - [Fine-tune models with Azure AI Foundry](../concepts/fine-tuning-overview.md)
-- [How to use Phi-3 family chat models](deploy-models-phi-3.md)
+- [How to generate chat completions with Azure AI model inference](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context)
+- [Featured models of Azure AI Foundry](../concepts/models-featured.md)

articles/ai-services/content-understanding/concepts/capabilities.md

@@ -156,7 +156,7 @@ Developers using Content Understanding should review Microsoft's policies on cus
 
 ## Next steps
 
-* Try processing your document content using Content Understanding in [Azure ](https://ai.azure.com/).
+* Try processing your document content using Content Understanding in [**Azure AI Foundry**](https://ai.azure.com/).
 * Learn to analyze content [**analyzer templates**](../quickstart/use-ai-foundry.md).
 * Review code sample: [**analyzer templates**](https://github.com/Azure-Samples/azure-ai-content-understanding-python/tree/main/analyzer_templates).
 * Take a look at our [**glossary**](../glossary.md)

articles/ai-services/content-understanding/concepts/retrieval-augmented-generation.md

@@ -41,7 +41,7 @@ Content Understanding offers a streamlined process to reason over large amounts
 
 * **Automation**. Content Understanding supports automation scenarios by converting unstructured content into structured data, which can be integrated into various workflows and applications. Confidence scores minimize human review and lower costs. For example, automate procurement and payment processes by extracting fields from invoices.
 
-* **Search and retrieval augmented generation (RAG)**. Content Understanding enables ingestion of content of any modality into the search index. The structured output representation improves the relevance for RAG scenarios.
+* **Search and retrieval-augmented generation (RAG)**. Content Understanding enables ingestion of content of any modality into the search index. The structured output representation improves the relevance for RAG scenarios.
 
 * **Analytics and reporting**: Content Understanding's extracted field outputs enhance analytics and reporting, allowing businesses to gain valuable insights, conduct deeper analysis, and make informed decisions based on accurate reports.
 

articles/ai-services/content-understanding/media/concepts/rag-architecture-1.png

@@ -54,6 +54,9 @@ items:
   - name: Video
     href: video/overview.md
     displayName: video, audio, voice, recognition, synthesis, speaker, identification, verification, diarization, transcription, translation, language, understanding, sentiment, analysis, emotion, detection, pronunciation, model
+  - name: Best practices
+    displayName: best practices, analyzers, optimization, fields
+    href: concepts/best-practices.md   
 - name: Concepts
   items:
     - name: Best practices
@@ -62,6 +65,14 @@ items:
     - name: Accuracy and confidence
       displayName: accuracy, confidence, analyzers, optimization, fields, scores
       href: concepts/accuracy-confidence.md
+    - name: Retrieval-augmented generation (RAG)
+      displayName: RAG, retrieval, augmented, generation, knowledge, base, search, index, vector
+      href: concepts/retrieval-augmented-generation.md
+- name: Tutorials
+  items:
+    - name: Build a retrieval-augmented solution
+      displayName: RAG, retrieval, augmented, generation, knowledge, base, search, index, vector
+      href: tutorial/build-rag-solution.md
 - name: Responsible AI
   items:
     - name: Transparency note