revise ai-template-get-started

Author: sdgilley

articles/ai-studio/how-to/develop/ai-template-get-started.md

@@ -7,8 +7,8 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 01/02/2025
-ms.reviewer: dantaylo
+ms.date: 02/20/2025
+ms.reviewer: varundua
 ms.author: sgilley
 author: sdgilley
 ---
@@ -19,40 +19,39 @@ author: sdgilley
 
 Streamline your code-first development with prebuilt, task-specific Azure AI templates. Benefit from using the latest features and best practices from Microsoft Azure AI, with popular frameworks like LangChain, prompt flow, and Semantic Kernel in multiple languages.
 
-> [!TIP]
-> Discover the latest templates in our curated [AZD templates collection](https://aka.ms/azd-ai-templates). Deploy them with a single command ```azd up``` using the [Azure Developer CLI](/azure/developer/azure-developer-cli/). 
-
-## Start with a sample application
+## Prerequisites
 
-Start with our sample applications! Choose the right template for your needs, then refer to the README in any of the following Azure Developer CLI enabled templates for more instructions and information.
+- [Azure subscription](https://azure.microsoft.com/free)
+- An [Azure AI Foundry project](../how-to/create-projects.md).
 
-### [Python](#tab/python)
+## Start with a sample application
 
-| Template      | App host | Tech stack | Description |
-| ----------- | ----------| ----------- | ------------|
-| [Azure AI Basic Template with Python](https://github.com/azure-samples/azureai-basic-python) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), Bicep | The app serves as a straightforward example of integrating Azure AI Services within a basic prompt-based application. This template walks you through building a simple chat app that utilizes models and prompts. The template also covers setting up the necessary infrastructure for the app, including creating an Azure AI Foundry Hub, configuring projects, and provisioning essential resources such as Azure AI Service, Azure Container Apps, Cognitive Search, and more. <br>You can build, deploy, and test it with a single command.  |
-| [Contoso Chat Retail copilot with Azure AI Foundry](https://github.com/Azure-Samples/contoso-chat) | [Azure Container Apps](/azure/container-apps/overview) | [Azure Cosmos DB](/azure/cosmos-db/index-overview), [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Azure AI Search](/azure/search/search-what-is-azure-search), Bicep  | A retailer conversation agent that can answer questions grounded in your product catalog and customer order history. This template uses a retrieval augmented generation architecture with cutting-edge models for chat completion, chat evaluation, and embeddings. Build, evaluate, and deploy, an end-to-end solution with a single command. | 
-| [Process Automation: speech to text and summarization with Azure AI Foundry](https://github.com/Azure-Samples/summarization-openai-python-prompflow) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Azure AI speech to text service](../../../ai-services/speech-service/index-speech-to-text.yml), Bicep  | An app for workers to report issues via text or speech, translating audio to text, summarizing it, and specify the relevant department. | 
-| [Multi-Modal Creative Writing copilot with Dalle](https://github.com/Azure-Samples/agent-openai-python-prompty) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure AI Search](/azure/search/search-what-is-azure-search), [Azure OpenAI Service](../../../ai-services/openai/overview.md), Bicep | demonstrates how to create and work with AI agents. The app takes a topic and instruction input and then calls a research agent, writer agent, and editor agent. |  
-| [Assistant API Analytics Copilot with Python and Azure AI Foundry](https://github.com/Azure-Samples/assistant-data-openai-python-promptflow) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) |  [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure AI Search](/azure/search/search-what-is-azure-search), [Azure OpenAI Service](../../../ai-services/openai/overview.md), Bicep| A data analytics chatbot based on the Assistants API. The chatbot can answer questions in natural language, and interpret them as queries on an example sales dataset. |
-<!-- remove for now
-| Function Calling with Prompty, LangChain, and Pinecone | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction), [Pinecone](https://www.pinecone.io/), Bicep  | Utilize the new Prompty tool, LangChain, and Pinecone to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses. | 
-| Function Calling with Prompty, LangChain, and Elastic Search | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Elastic Search](https://www.elastic.co/elasticsearch), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction) , Bicep  | Utilize the new Prompty tool, LangChain, and Elasticsearch to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses |
--->
+1. Go to [Azure AI Foundry portal](https://ai.azure.com).
+1. Open your project in Azure AI Foundry portal.
+1. On the left menu, select **Code**.
+1. Find the solution template you want to use.
+1. Select **Open in Github** to view the entire sample application.
+1. Or, clone the repository to your local machine with the provided command.
+1. In some cases, you can also view a step-by-step tutorial that explains the AI code.
 
+Once you're looking at the GitHub repository for your sample, refer to the README for more instructions and information on how to deploy your own version of the application.
 
-### [C#](#tab/csharp)
+Instructions will vary by sample, but most will include how to:
 
-| Template      | App host | Tech stack | Description |
-| ----------- | ----------| ----------- | -------------- |
-| [Contoso Chat Retail copilot with .NET and Semantic Kernel](https://github.com/Azure-Samples/contoso-chat-csharp-prompty) | [Azure Container Apps](/azure/container-apps/overview) | [Azure Cosmos DB](/azure/cosmos-db/index-overview), [Azure Monitor](/azure/azure-monitor/overview), [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure Container Apps](/azure/container-apps/overview), [Azure AI Search](/azure/search/search-what-is-azure-search), [Azure OpenAI Services](../../../ai-services/openai/overview.md), [Semantic Kernel](/semantic-kernel/overview/?tabs=Csharp), Bicep | A retailer conversation agent that can answer questions grounded in your product catalog and customer order history. This template uses a retrieval augmented generation architecture with cutting-edge models for chat completion, chat evaluation, and embeddings. Build, evaluate, and deploy, an end-to-end solution with a single command. |
-| [Process Automation: speech to text and summarization with .NET and GPT 3.5 Turbo](https://github.com/Azure-Samples/summarization-openai-csharp-prompty) | [Azure Container Apps](/azure/container-apps/overview) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Azure AI speech to text service](../../../ai-services/speech-service/index-speech-to-text.yml), Bicep | An app for workers to report issues via text or speech, translating audio to text, summarizing it, and specify the relevant department. |
+* Open the solution in the location of your choice:
+  * GitHub Codespaces
+  * VS Code Dev Containers
+  * Your local IDE
+* Deploy the application to Azure
+* Steps for how to test the app
 
----
+The README also includes information about the application, such as the use case, architecture, and pricing information.
 
+> [!TIP]
+> Discover more templates in our curated [AZD templates collection](https://azure.github.io/ai-app-templates). Deploy them with a single command ```azd up``` using the [Azure Developer CLI](/azure/developer/azure-developer-cli/).
 
 ## Related content
 
-- [Get started building a chat app using the prompt flow SDK](../../quickstarts/get-started-code.md)
+- [Get started building a chat app using the Azure AI Foundry SDK](../../quickstarts/get-started-code.md)
 - [Work with projects in VS Code](vscode.md)
 - [Connections in Azure AI Foundry portal](../../concepts/connections.md)