@@ -1,6 +1,6 @@
 ---
 title: Use the Azure AI model inference endpoint
-titleSuffix: Azure AI studio
+titleSuffix: Azure AI Foundry
 description: Learn about to use the Azure AI model inference endpoint and how to configure it.
 ms.service: azure-ai-studio
 ms.topic: conceptual

@@ -1,6 +1,6 @@
 ---
 title: Content Safety in Azure AI Studio overview
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Azure AI Content Safety in Azure AI Studio to detect harmful user-generated and AI-generated content in applications and services.
 manager: nitinme
 ms.service: azure-ai-studio

@@ -1,7 +1,7 @@
 ### YamlMime:FAQ
 metadata:
   title: Azure AI model inference service frequently asked questions
-  titleSuffix: Azure AI studio
+  titleSuffix: Azure AI Foundry
   description: Get answers to the most popular questions about Azure AI model service
   #services: cognitive-services
   ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to use Azure AI services in AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Azure AI services in AI Studio. You can use existing Azure AI services resources in AI Studio by creating a connection to the resource.
 manager: nitinme
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to use Azure OpenAI Service in AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Azure OpenAI Service in AI Studio.
 manager: nitinme
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: What is Azure AI model inference service?
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Apply advanced language models to variety of use cases with Azure AI model inference
 manager: nitinme
 author: mrbullwinkle

@@ -1,6 +1,6 @@
 ---
 title: Azure OpenAI in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn about using Azure OpenAI models in Azure AI Studio, including when to use a project and when to use without a project.
 manager: scottpolly
 keywords: Azure AI services, cognitive, Azure OpenAI

@@ -1,6 +1,6 @@
 ---
 title: Manage, collaborate, and organize with hubs
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces concepts about Azure AI Studio hubs for your AI Studio projects.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Studio architecture
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn about the architecture of Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Distillation in AI Studio (preview)
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to do distillation in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Synthetic data generation in AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to generate a synthetic dataset in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Connections in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces connections in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Studio content filtering
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn about the content filtering capabilities of Azure OpenAI in Azure AI Studio.
 manager: nitinme
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Deploy models in Azure AI studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn about deploying models in Azure AI studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Customer-Managed Keys for Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn about using customer-managed keys for encryption to improve data security with Azure AI Studio.
 author: Blackmist
 ms.author: larryfr

@@ -1,6 +1,6 @@
 ---
 title: Evaluation of generative AI applications with Azure AI Foundry
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Explore the broader domain of monitoring and evaluating large language models through the establishment of precise metrics, the development of test sets for measurement, and the implementation of iterative testing.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Evaluation and monitoring metrics for generative AI
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Discover the supported built-in metrics for evaluating large language models, understand their application and usage, and learn how to interpret them effectively.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Fine-tuning in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces fine-tuning of models in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio
@@ -14,88 +14,76 @@ ms.author: sgilley
 author: sdgilley
 ---
 
-# Fine-tune models in Azure AI Studio
+# Fine-tune models with Azure AI Foundry
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-Fine-tuning retrains an existing large language model (LLM) by using example data. The result is a new, custom LLM that's optimized for the provided examples.
+Fine-tuning refers to customizing a pre-trained generative AI model with additional training on a specific task or new dataset for enhanced performance, new skills, or improved accuracy. The result is a new, custom GenAI model that's optimized based on the provided examples.
 
-This article can help you decide whether or not fine-tuning is the right solution for your use case. This article also describes how [Azure AI Studio](https://ai.azure.com) can support your fine-tuning needs.
+Consider fine-tuning GenAI models to:
+- Scale and adapt to specific enterprise needs
+- Reduce false positives as tailored models are less likely to produce inaccurate or irrelevant responses
+- Enhance the model's accuracy for domain-specific tasks
+- Save time and resources with faster and more precise results
+- Get more relevant and context-aware outcomes as models are fine-tuned for specific use cases
 
-In this article, fine-tuning refers to *supervised fine-tuning*, not to continuous pretraining or reinforcement learning through human feedback (RLHF). Supervised fine-tuning is the process of retraining pretrained models on specific datasets. The purpose is typically to improve model performance on specific tasks or to introduce information that wasn't well represented when you originally trained the base model.
+[Azure AI Foundry](https://ai.azure.com) offers several models across model providers enabling you to get access to the latest and greatest in the market. You can discover supported models for fine-tuning through our model catalog by using the **Fine-tuning tasks** filter and clicking into the model card to learn detailed information about each model. Specific models may be subjected to regional constraints, [view this list for more details](#supported-models-for-fine-tuning). 
 
-## Getting starting with fine-tuning
+:::image type="content" source="../media/concepts/model-catalog-fine-tuning.png" alt-text="Screenshot of Azure AI Foundry model catalog and filtering by Fine-tuning tasks." lightbox="../media/concepts/model-catalog-fine-tuning.png":::
 
-When you're deciding whether or not fine-tuning is the right solution for your use case, it's helpful to be familiar with these key terms:
+This article will walk you through use-cases for fine-tuning and how this can help you in your GenAI journey.
 
-- [Prompt engineering](../../ai-services/openai/concepts/prompt-engineering.md) is a technique that involves designing prompts for natural language processing models. This process improves accuracy and relevancy in responses, to optimize the performance of the model.
-- [Retrieval-augmented generation (RAG)](../concepts/retrieval-augmented-generation.md) improves LLM performance by retrieving data from external sources and incorporating it into a prompt. RAG can help businesses achieve customized solutions while maintaining data relevance and optimizing costs.
-
-Fine-tuning is a great way to get higher quality results while reducing latency. The following questions can help you better understand why fine-tuning and evaluate whether you're ready for fine-tuning through the process. You can use these questions to guide your next steps.
-
-### Why do you want to fine-tune a model?
-
-Before you begin fine-tuning a model, consider if you've identified shortcomings when using a base model. These shortcomings can include: an inconsistent performance on edge cases, inability to fit enough prompts in the context window to steer the model, or high latency.
-
-Base models are already pre-trained on vast amounts of data, but most times you will add instructions and examples to the prompt to get the quality responses that you're looking for. This process of "few-shot learning" can be improved with fine-tuning.
-
-Fine-tuning allows you to train a model with many more examples. You can tailor your examples to meet your specific use-case. This can help you reduce the number of tokens in the prompt leading to potential cost savings and requests with lower latency.
-
-Use cases for fine-tuning a model can be:
-- Steering the model to output content in a specific and customized style, tone, or format.
+## Getting started with fine-tuning
 
-If you identify cost as your primary motivator, proceed with caution. Fine-tuning might reduce costs for certain use cases by shortening prompts or allowing you to use a smaller model. But typically there's a higher upfront cost to training, and you have to pay for hosting your own custom model. 
-
-### What isn't working with alternate approaches?
-
-Understanding where prompt engineering falls short should provide guidance on approaching your fine-tuning. Is the base model failing on edge cases or exceptions? Is the base model not consistently providing output in the right format, and you can't fit enough examples in the context window to fix it?
-
-Examples of failure with the base model and prompt engineering can help you identify the data that they need to collect for fine-tuning, and how you should be evaluating your fine-tuned model.
-
-Here's an example: A customer wants to use GPT-3.5 Turbo to turn natural language questions into queries in a specific, nonstandard query language. The customer provides guidance in the prompt ("Always return GQL") and uses RAG to retrieve the database schema. However, the syntax isn't always correct and often fails for edge cases. The customer collects thousands of examples of natural language questions and the equivalent queries for the database, including cases where the model failed before. The customer then uses that data to fine-tune the model. Combining the newly fine-tuned model with the engineered prompt and retrieval brings the accuracy of the model outputs up to acceptable standards for use.
-
-### What have you tried so far?
-
-Fine-tuning is an advanced capability, not the starting point for your generative AI journey. You should already be familiar with the basics of using LLMs. You should start by evaluating the performance of a base model with prompt engineering and/or RAG to get a baseline for performance.
+When starting out on your generative AI journey, we recommend you begin with prompt engineering and RAG to familiarize yourself with base models and its capabilities. 
+- [Prompt engineering](../../ai-services/openai/concepts/prompt-engineering.md) is a technique that involves designing prompts using tone and style details, example responses, and intent mapping for natural language processing models. This process improves accuracy and relevancy in responses, to optimize the performance of the model.
+- [Retrieval-augmented generation (RAG)](../concepts/retrieval-augmented-generation.md) improves LLM performance by retrieving data from external sources and incorporating it into a prompt. RAG can help businesses achieve customized solutions while maintaining data relevance and optimizing costs.
 
-Having a baseline for performance without fine-tuning is essential for knowing whether or not fine-tuning improves model performance. Fine-tuning with bad data makes the base model worse, but without a baseline, it's hard to detect regressions.
+As you get comfortable and begin building your solution, it's important to understand where prompt engineering falls short and that will help you realize if you should try fine-tuning. 
+- Is the base model failing on edge cases or exceptions? 
+- Is the base model not consistently providing output in the right format?
+- Is it difficult to fit enough examples in the context window to steer the model?
+- Is there high latency?
 
-Before you begin fine-tuning a model, you need to ensure:
+Examples of failure with the base model and prompt engineering can help you identify the data to collect for fine-tuning and establish a performance baseline that you can evaluate and compare your fine-tuned model against. Having a baseline for performance without fine-tuning is essential for knowing whether or not fine-tuning improves model performance.
 
-- You can demonstrate evidence and knowledge of using prompt engineering and RAG-based approaches on your LLM.
-- You can share specific experiences and challenges with techniques other than fine-tuning that you tried for your use case.
-- You have quantitative assessments of baseline performance, whenever possible.
-- You have a labeled dataset that corresponds with the specific usecase you want to train your LLM. 
+Here's an example: 
 
-### What data are you going to use for fine-tuning?
+_A customer wants to use GPT-3.5 Turbo to turn natural language questions into queries in a specific, nonstandard query language. The customer provides guidance in the prompt ("Always return GQL") and uses RAG to retrieve the database schema. However, the syntax isn't always correct and often fails for edge cases. The customer collects thousands of examples of natural language questions and the equivalent queries for the database, including cases where the model failed before. The customer then uses that data to fine-tune the model. Combining the newly fine-tuned model with the engineered prompt and retrieval brings the accuracy of the model outputs up to acceptable standards for use._
 
-Even with a great use case, fine-tuning is only as good as the quality of the data that you can provide. You need to be willing to invest the time and effort to make fine-tuning work. Different models require different data volumes, but you often need to be able to provide fairly large quantities of high-quality curated data. In supervised fine-tuning, a generic moddel is trained on a topic specific labeled dataset. The model with adjust it's parameters to the new data and apply pre-existing knowledge when outputting new content. 
+### Use cases
 
-Another important point is that even with high-quality data, if your data isn't in the necessary format for fine-tuning, you need to commit engineering resources for the formatting. 
+Base models are already pre-trained on vast amounts of data and most times you'll add instructions and examples to the prompt to get the quality responses that you're looking for - this process is called "few-shot learning". Fine-tuning allows you to train a model with many more examples that you can tailor to meet your specific use-case, thus improving on few-shot learning. This can reduce the number of tokens in the prompt leading to potential cost savings and requests with lower latency. 
 
-You might be ready for fine-tuning if:
+Turning natural language into a query language is just one use case where you can  _show not tell_ the model how to behave. Here are some additional use cases:
+- Improve the model's handling of retrieved data
+- Steer model to output content in a specific style, tone, or format
+- Improve the accuracy when you look up information
+- Reduce the length of your prompt
+- Teach new skills (i.e. natural language to code)
 
-- You identified a dataset for fine-tuning.
-- Your dataset is in the appropriate format for training on your existing model.
-- You employed some level of curation to ensure dataset quality.
+If you identify cost as your primary motivator, proceed with caution. Fine-tuning might reduce costs for certain use cases by shortening prompts or allowing you to use a smaller model. But there may be a higher upfront cost to training, and you have to pay for hosting your own custom model. 
 
+### Steps to fine-tune a model
+Here are the general steps to fine-tune a model:
+1. Based on your use case, choose a model that supports your task
+2. Prepare and upload training data
+3. (Optional) Prepare and upload validation data
+4. (Optional) Configure task parameters
+5. Train your model. 
+6. Once completed, review metrics and evaluate model. If the results don't meet your benchmark, then go back to step 2.
+7. Use your fine-tuned model
 
-### How will you measure the quality of your fine-tuned model?
+It's important to call out that fine-tuning is heavily dependent on the quality of data that you can provide. It's best practice to provide hundreds, if not thousands, of training examples to be successful and get your desired results.
 
-There isn't a single right answer to this question, but you should have clearly defined goals for what success with fine-tuning looks like. Ideally, this effort shouldn't just be qualitative. It should include quantitative measures of success, like using a holdout set of data for validation, in addition to user acceptance testing or A/B testing the fine-tuned model against a base model. 
+## Supported models for fine-tuning
 
-## Supported models for fine-tuning in Azure AI Studio
+Now that you know when to use fine-tuning for your use case, you can go to Azure AI Studio to find models available to fine-tune. Fine-tuning is available in specific Azure regions for some models. To fine-tune such models, a user must have a hub/project in the region where the model is available for fine-tuning. See [Region availability for models in serverless API endpoints | Azure AI Studio](../how-to/deploy-models-serverless-availability.md) for detailed information.
 
-Now that you know when to use fine-tuning for your use case, you can go to Azure AI Studio to find models available to fine-tune. The following table describes models that you can fine-tune in Azure AI Studio, along with the regions where you can fine-tune them.
+For details about Azure OpenAI models that are available for fine-tuning, see the [Azure OpenAI Service models documentation](../../ai-services/openai/concepts/models.md#fine-tuning-models) or the [Azure OpenAI models table](#fine-tuning-azure-openai-models) later in this guide.
 
-| Model family | Model ID | Fine-tuning regions |
-| --- | --- | --- |
-| [Azure OpenAI models](../../ai-services/openai/how-to/fine-tuning.md?context=/azure/ai-studio/context/context) | Azure OpenAI Service models that you can fine-tune include among others `gpt-4` and `gpt-4o-mini`.<br/><br/>For details about Azure OpenAI models that are available for fine-tuning, see the [Azure OpenAI Service models documentation](../../ai-services/openai/concepts/models.md#fine-tuning-models) or the [Azure OpenAI models table](#fine-tuning-azure-openai-models) later in this guide. | Azure OpenAI Service models that you can fine-tune include among others North Central US and Sweden Central.<br/><br/>The supported regions might vary if you use Azure OpenAI models in an AI Studio project versus outside a project.<br/><br/>For details about fine-tuning regions, see the [Azure OpenAI Service models documentation](../../ai-services/openai/concepts/models.md#fine-tuning-models). |
-| [Phi-3 family models](../how-to/fine-tune-phi-3.md) | `Phi-3-mini-4k-instruct`<br/>`Phi-3-mini-128k-instruct`<br/>`Phi-3-medium-4k-instruct`<br/>`Phi-3-medium-128k-instruct` | East US2 |
-| [Meta Llama 2 family models](../how-to/fine-tune-model-llama.md) | `Meta-Llama-2-70b`<br/>`Meta-Llama-2-7b`<br/>`Meta-Llama-2-13b` <br/> `Llama-2-7B-chat` <br> `Llama-2-70B-chat` | West US3 |
-| [Meta Llama 3.1 family models](../how-to/fine-tune-model-llama.md) | `Meta-Llama-3.1-70b-Instruct`<br/>`Meta-Llama-3.1-8b-Instruct` | West US3 |
 
-This table provides more details about the Azure OpenAI Service models that support fine-tuning and the regions where fine-tuning is available.
+For the Azure OpenAI  Service models that you can fine tune, supported regions for fine-tuning include North Central US, Sweden Central, and more.
 
 ### Fine-tuning Azure OpenAI models
 

@@ -1,6 +1,6 @@
 ---
 title: Management center overview
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: "The management center in Azure AI Studio provides a centralized hub for governance and management activities."
 author: Blackmist
 ms.author: larryfr

@@ -1,6 +1,6 @@
 ---
 title: Explore model benchmarks in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces benchmarking capabilities and the model benchmarks experience in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Role-based access control in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces role-based access control in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Retrieval augmented generation in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces retrieval augmented generation for use in generative AI applications.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Transparency Note for Azure AI Studio safety evaluations
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Azure AI Studio safety evaluations intended purpose, capabilities, limitations and how to achieve the best performance.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Tracing in Azure AI Inference SDK
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides an overview of tracing with the Azure AI Inference SDK.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Vulnerability management
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how Azure AI Studio manages vulnerabilities in images that the service provides, and how you can get the latest security updates for the components that you manage.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,7 +1,7 @@
 ### YamlMime:FAQ
 metadata:
   title: Azure AI frequently asked questions
-  titleSuffix: Azure AI Studio
+  titleSuffix: Azure AI Foundry
   description: Get answers to the most popular questions about Azure AI services.
   manager: scottpolly
   ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to access on-premises resources
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to configure an Azure AI Studio managed network to securely allow access to your on-premises resources.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Autoscale Azure AI limits
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how you can manage and increase quotas for resources with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to use model benchmarking in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: In this article, you learn to compare benchmarks across models and datasets, using the model benchmarks tool in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Control AI model deployment with built-in policies
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: "Learn how to use built-in Azure policies to control what managed AI Services (MaaS) and Model-as-a-Platform (MaaP) AI models can be deployed in Azure AI Studio."
 author: Blackmist
 ms.author: larryfr

@@ -1,6 +1,6 @@
 ---
 title: Data, privacy, and security for use of models through the model catalog in AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Get details about how data that customers provide is processed, used, and stored when a user deploys a model from the model catalog.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to configure a managed network for Azure AI Studio hubs
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to configure a managed network for Azure AI Studio hubs.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to configure a private link for an Azure AI Studio hub
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to configure a private link for Azure AI Studio hubs. A private link is used to secure communication with the Azure AI Studio hub.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to add a new connection in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to add a new connection in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Plan and manage costs for Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to plan for and manage costs for Azure AI Studio by using cost analysis in the Azure portal.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Create an Azure AI Studio hub using a Bicep template
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Use a Microsoft Bicep template to create a new Azure AI Studio hub.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to create and manage an Azure AI Studio hub
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to create and manage an Azure AI Studio hub from the Azure portal or from the AI Studio. Your developers can then create projects from the hub.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -3,7 +3,7 @@ title: 'Use Terraform to create an Azure AI Studio hub'
 description: In this article, you create an Azure AI Studio hub, an Azure AI Studio project, an AI services resource, and more resources.
 ms.topic: how-to
 ms.date: 07/12/2024
-titleSuffix: Azure AI Studio 
+titleSuffix: Azure AI Foundry 
 ms.service: azure-ai-studio 
 manager: scottpolly 
 ms.reviewer: andyaviles 

@@ -1,6 +1,6 @@
 ---
 title: Create and manage prompt flow compute sessions
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: In this article, learn how to create and manage compute sessions to run prompt flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to create and manage compute instances in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to create and manage compute instances in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Create an Azure AI Studio project in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article describes how to create an Azure AI Studio project so you can work with generative AI in the cloud.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Create a secure hub
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Create an Azure AI Studio hub inside a managed virtual network. The managed virtual network secures access to managed resources such as computes.
 ms.service: azure-ai-studio
 ms.custom:

@@ -1,6 +1,6 @@
 ---
 title: Control AI model deployment with custom policies
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: "Learn how to use custom Azure Policies to control Azure AI services and Azure OpenAI model deployment with Azure AI Studio."
 author: Blackmist
 ms.author: larryfr

@@ -1,6 +1,6 @@
 ---
 title: How to add and manage data in your Azure AI Studio project
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to add and manage data in your Azure AI Studio project.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: 'Use your image data with Azure OpenAI Service'
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Use this article to learn about using your image data for image generation in Azure AI Studio.
 manager: nitinme
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to use Cohere Command chat models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Cohere Command chat models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to use Cohere Embed V3 models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Cohere Embed V3 models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to deploy Cohere Rerank models as serverless APIs
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn to deploy and use Cohere Rerank models with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to use Jais chat models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Jais chat models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to deploy AI21's Jamba family models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: How to deploy AI21's Jamba family models with Azure AI Studio
 manager: scottpolly
 ms.service: azure-machine-learning

@@ -1,6 +1,6 @@
 ---
 title: How to use the Meta Llama family of models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use the Meta Llama family of models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to use Mistral Nemo chat model with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Mistral Nemo chat model with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to use Mistral-7B and Mixtral chat models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Mistral-7B and Mixtral chat models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to use Mistral premium chat models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Mistral premium chat models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to deploy Azure OpenAI models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to deploy Azure OpenAI models with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to use Phi-3.5 chat model with vision with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Phi-3.5 chat model with vision with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to use Phi-3 chat model with vision with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Phi-3 chat model with vision with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to use Phi-3 family chat models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use Phi-3 family chat models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: Region availability for models in Serverless API endpoints
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn about the regions where each model is available for deployment in serverless API endpoints via Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Consume deployed serverless API endpoints from a different project or hub
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to consume deployed serverless API endpoints from a different project or hub.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Deploy models as serverless APIs
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn to deploy models as serverless APIs, using Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to deploy TimeGEN-1 model with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to deploy TimeGEN-1 with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to use tsuzumi-7b models with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use tsuzumi-7b models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to get started with an AI template
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to get started with an AI template.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to add a new connection in AI Studio using the Azure Machine Learning SDK
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to add connections to other resources using the Azure Machine Learning SDK.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to create a hub using the Azure Machine Learning SDK/CLI
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to create an AI Studio hub using the Azure Machine Learning SDK and Azure CLI extension.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to build and consume an index using code
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to build and consume an index using code.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Develop application with LangChain and Azure AI studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article explains how to use LangChain with models deployed in Azure AI studio to build advance intelligent applications.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Develop application with LlamaIndex and Azure AI studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article explains how to use LlamaIndex with models deployed in Azure AI studio to build advance intelligent applications.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to get started with Azure AI Foundry SDK
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides an overview of the Azure AI Foundry SDK and how to get started using it.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to generate synthetic and simulated data for evaluation
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to generate synthetic data to run simulations to evaluate the performance and safety of your generative AI application.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to trace your application with Azure AI Inference SDK
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to trace your application with  Azure AI Inference SDK.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to enable tracing and collect feedback for a flow deployment
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to enable tracing and collect feedback for a flow deployment in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Visualize your traces
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to visualize your traces.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Work with Azure AI Studio projects in VS Code
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to get started with Azure AI Studio projects in VS Code.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: "Disable shared key access to the hub storage account"
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: "Disable shared key access to the default storage account used by your Azure AI Studio hub and projects."
 author: Blackmist
 ms.author: larryfr

@@ -1,6 +1,6 @@
 ---
 title: Customer enabled disaster recovery
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to plan for disaster recovery for Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to evaluate generative AI models and applications with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Evaluate your generative AI models and applications with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to manually evaluate prompts in Azure AI Studio playground
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Quickly test and evaluate prompts in Azure AI Studio playground.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to view evaluation results in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to view evaluation results in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Fine-tune Llama models in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to fine-tune Meta Llama models in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Fine-tune tsuzumi-7b model in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to fine-tune tsuzumi-7b in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Fine-tune Phi-3 models in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces fine-tuning Phi-3 models in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Submit batch run and evaluate a flow
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to submit batch run and use built-in evaluation methods in prompt flow to evaluate how well your flow performs with a large dataset with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Deploy a flow as a managed online endpoint for real-time inference
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to deploy a flow as a managed online endpoint for real-time inference with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Develop an evaluation flow
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to customize or create your own evaluation flow tailored to your tasks and objectives, and then use in a batch run as an evaluation method in prompt flow with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to build with prompt flow
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to build with prompt flow.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Process images in prompt flow
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use images in prompt flow.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Tune prompts using variants
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to tune prompts using variants in Prompt flow with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to deploy and use CXRReportGen healthcare AI model with AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use CXRReportGen Healthcare AI Model with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to deploy and use MedImageInsight healthcare AI model with AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use MedImageInsight Healthcare AI Model with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to deploy and use MedImageParse healthcare AI model with AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use MedImageParse Healthcare AI Model with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: Foundation models for healthcare in AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn about AI models that are applicable to the health and life science industry.
 ms.service: azure-ai-studio
 manager: scottpolly

@@ -1,6 +1,6 @@
 ---
 title: How to build and consume vector indexes in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to create and use a vector index for performing Retrieval Augmented Generation (RAG).
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Explore the model catalog in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces foundation model capabilities and the model catalog in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Monitor quality and token usage of deployed prompt flow applications (preview)
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to monitor quality and token usage of deployed prompt flow applications with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Continuously Monitor your Generative AI Applications
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to use online and remote evaluation to continuously monitor Generative AI Applications.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure OpenAI GPT-4 Turbo with Vision tool in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces you to the Azure OpenAI GPT-4 Turbo with Vision tool for flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Content Safety tool for flows in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces you to the Content Safety tool for flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Embedding tool for flows in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces you to the Embedding tool for flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Index Lookup tool for flows in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces you to the Index Lookup tool for flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: LLM tool for flows in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces you to the large language model (LLM) tool for flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Overview of prompt flow tools in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn about prompt flow tools that are available in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Prompt tool for flows in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces you to the Prompt tool for flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Python tool for flows in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces you to the Python tool for flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Rerank tool for flows in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces you to the Rerank tool for flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Serp API tool for flows in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces you to the Serp API tool for flows in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Troubleshoot guidance for prompt flow
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article addresses frequent questions about prompt flow usage.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Prompt flow in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article introduces prompt flow in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Manage and increase quotas for resources with Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to manage and increase quotas for resources with Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Securely use playground chat
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to securely use the Azure AI Studio playground chat on your own data. 
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: How to troubleshoot your deployments and monitors in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to troubleshoot your deployments and monitors in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Troubleshoot private endpoint connection
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: 'Learn how to troubleshoot connectivity problems to a project that is configured with a private endpoint.'
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Use blocklists in AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to create custom blocklists in Azure AI Studio as part of your content filtering configurations.
 manager: nitinme
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Get started building a chat app using the Azure AI Foundry SDK
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to build a custom chat app in Python using the Azure AI SDK.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Use the chat playground in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Use this article to learn how to deploy a chat model and use it in the chat playground in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Hear and speak with chat models in the Azure AI Studio chat playground
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Hear and speak with chat models in the Azure AI Studio chat playground.
 manager: nitinme
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Get started using GPT-4 Turbo with Vision on your images and videos in Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Get started using GPT-4 Turbo with Vision on your images and videos in Azure AI Studio.
 manager: nitinme
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Model Inference API
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn about how to use the Azure AI Model Inference API
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Model Inference Chat Completions
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Reference for Azure AI Model Inference Chat Completions API
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Model Inference Completions
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Reference for Azure AI Model Inference Completions API
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Model Inference Embeddings API
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Reference for Azure AI Model Inference Embeddings API
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Model Inference Image Embeddings
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Reference for Azure AI Model Inference Image Embeddings API
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Model Inference Get Info
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Reference for Azure AI Model Inference Get Info API
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Studio feature availability across clouds regions
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: This article lists Azure AI Studio feature availability across clouds regions.
 manager: scottpolly
 ms.service: azure-ai-studio
@@ -38,6 +38,7 @@ Azure AI Studio is currently available in the following Azure regions. You can c
 - South Central US
 - Sweden Central
 - Switzerland North
+- UAE North
 - UK South
 - West Europe
 - West US

@@ -1,6 +1,6 @@
 ---
 title: Responsible AI for Azure AI Studio
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Learn how to use AI responsibly with Azure AI Studio.
 manager: nitinme
 keywords: Azure AI services, cognitive

@@ -1,6 +1,6 @@
 ---
 title: "Part 2: Build a ca custom knowledge retrieval (RAG) app with the Azure AI Foundry SDK"
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description:  Learn how to build a RAG-based chat app using the Azure AI Foundry SDK. This tutorial is part 2 of a 3-part tutorial series.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: "Part 1: Set up project and development environment to build a custom knowledge retrieval (RAG) app"
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description:  Build a custom chat app using the Azure AI Foundry SDK. Part 1 of a 3-part tutorial series, which shows how to create the resources you'll need for parts 2 and 3.
 manager: scottpolly
 ms.service: azure-ai-studio

@@ -1,6 +1,6 @@
 ---
 title: "Part 3: Evaluate and deploy chat app with the Azure AI SDK"
-titleSuffix: Azure AI Studio
+titleSuffix: Azure AI Foundry
 description: Evaluate and deploy a custom chat app with the prompt flow SDK. This tutorial is part 3 of a 3-part tutorial series.
 manager: scottpolly
 ms.service: azure-ai-studio
@@ -152,6 +152,16 @@ You can also look at the individual rows and see metric scores per row, and view
 
 For more information about evaluation results in AI Studio, see [How to view evaluation results in AI Studio](../how-to/evaluate-results.md).
 
+## Iterate and improve
+
+Notice that the responses are not well grounded. In many cases, the model replies with a question rather than an answer. This is a result of the prompt template instructions. 
+ 
+* In your **assets/grounded_chat.prompty** file, find the sentence "If the question is related to outdoor/camping gear and clothing but vague, ask for clarifying questions instead of referencing documents."
+* Change the sentence to "If the question is related to outdoor/camping gear and clothing but vague, try to answer based on the reference documents, then ask for clarifying questions."  
+* Save the file and re-run the evaluation script.
+
+Try other modifications to the prompt template, or try different models, to see how the changes affect the evaluation results.
+
 ## Clean up resources
 
 To avoid incurring unnecessary Azure costs, you should delete the resources you created in this tutorial if they're no longer needed. To manage resources, you can use the [Azure portal](https://portal.azure.com?azure-portal=true).