updating examples

Author: aahill

articles/ai-services/openai/includes/use-your-data-dotnet.md

@@ -36,7 +36,7 @@ var chatCompletionsOptions = new ChatCompletionsOptions()
 {
     Messages =
     {
-        new ChatRequestUserMessage("What are the differences between Azure Machine Learning and Azure AI services?"),
+        new ChatRequestUserMessage("What are my available health plans?"),
     },
     AzureExtensionsOptions = new AzureChatExtensionsOptions()
     {
@@ -94,7 +94,7 @@ dotnet run program.cs
 ```output
 Answer from assistant:
 ===
-Azure Machine Learning is a cloud-based service that provides tools and services to build, train, and deploy machine learning models. It offers a collaborative environment for data scientists, developers, and domain experts to work together on machine learning projects. Azure Machine Learning supports various programming languages, frameworks, and libraries, including Python, R, TensorFlow, and PyTorch [^1^].
+The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans [^1^].
 ===
 Context information (e.g. citations) from chat extensions:
 ===
@@ -113,7 +113,7 @@ tool: {
     },
     ...
   ],
-  "intent": "[\u0022What are the differences between Azure Machine Learning and Azure AI services?\u0022]"
+  "intent": "[\u0022What are my available health plans?\u0022]"
 }
 ===
 ```
@@ -143,7 +143,7 @@ var chatCompletionsOptions = new ChatCompletionsOptions()
     DeploymentName = deploymentName,
     Messages =
     {
-        new ChatRequestUserMessage("What are the differences between Azure Machine Learning and Azure AI services?"),
+        new ChatRequestUserMessage("What are my available health plans?"),
     },
     AzureExtensionsOptions = new AzureChatExtensionsOptions()
     {

articles/ai-services/openai/includes/use-your-data-go.md

@@ -76,7 +76,7 @@ ms.date: 03/07/2024
    
    	resp, err := client.GetChatCompletions(context.TODO(), azopenai.ChatCompletionsOptions{
    		Messages: []azopenai.ChatRequestMessageClassification{
-   			&azopenai.ChatRequestUserMessage{Content: azopenai.NewChatRequestUserMessageContent("What are the differences between Azure Machine Learning and Azure AI services?")},
+   			&azopenai.ChatRequestUserMessage{Content: azopenai.NewChatRequestUserMessageContent("What are my available health plans?")},
    		},
    		MaxTokens: to.Ptr[int32](512),
    		AzureExtensionsOptions: []azopenai.AzureChatExtensionConfigurationClassification{

articles/ai-services/openai/includes/use-your-data-javascript.md

@@ -51,7 +51,7 @@ async function main(){
   const client = new OpenAIClient(endpoint, new AzureKeyCredential(azureApiKey));
 
   const messages = [
-    { role: "user", content: "Tell me something interesting" },
+    { role: "user", content: "What are my available health plans?" },
   ];
 
   console.log(`Message: ${messages.map((m) => m.content).join("\n")}`);
@@ -102,8 +102,8 @@ node.exe ChatWithOwnData.js
 ## Output
 
 ```output
-Message: Tell me something interesting
-Based on the retrieved document, an interesting fact is...
+Message: What are my available health plans?
+The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans.
 
 ```
 

articles/ai-services/openai/includes/use-your-data-powershell.md

@@ -49,7 +49,7 @@ To trigger a response from the model, you should end with a user message indicat
     messages = @(
             @{
                 role = 'user'
-                content = 'How do you query REST using PowerShell'
+                content = 'What are my available health plans?'
             }
     )
    } | convertto-json -depth 5
@@ -69,7 +69,7 @@ To trigger a response from the model, you should end with a user message indicat
 ### Example output
 
 ```text
-To query a RESTful web service using PowerShell, you can use the `Invoke-RestMethod` cmdlet. This cmdlet sends HTTP and HTTPS requests to RESTful web services and processes the response based on the data type.
+The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans.
 ```
 
 > [!IMPORTANT]

articles/ai-services/openai/includes/use-your-data-python.md

@@ -86,7 +86,7 @@ pip install python-dotenv
    setup_byod(aoai_deployment_id)
 
    completion = openai.ChatCompletion.create(
-       messages=[{"role": "user", "content": "What are the differences between Azure Machine Learning and Azure AI services?"}],
+       messages=[{"role": "user", "content": "What are my available health plans?"}],
        deployment_id=os.environ.get("AZURE_OPENAI_DEPLOYMENT_ID"),
        dataSources=[  # camelCase is intentional, as this is the format the API expects
            {
@@ -126,7 +126,7 @@ completion = client.chat.completions.create(
     messages=[
         {
             "role": "user",
-            "content": "How is Azure machine learning different than Azure OpenAI?",
+            "content": "What are my available health plans?",
         },
     ],
     extra_body={

articles/ai-services/openai/includes/use-your-data-rest.md

@@ -39,7 +39,7 @@ curl -i -X POST $AZURE_OPENAI_ENDPOINT/openai/deployments/$AZURE_OPENAI_DEPLOYME
     "messages": [
         {
             "role": "user",
-            "content": "Tell me an interesting fact"
+            "content": "What are my available health plans?"
         }
     ]
 }
@@ -60,7 +60,7 @@ curl -i -X POST $AZURE_OPENAI_ENDPOINT/openai/deployments/$AZURE_OPENAI_DEPLOYME
             "finish_reason": "stop",
             "message": {
                 "role": "assistant",
-                "content": "An interesting fact from the retrieved document is... [doc1].",
+                "content": "The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans. [doc1].",
                 "end_turn": true,
                 "context": {
                     "citations": [
@@ -72,7 +72,7 @@ curl -i -X POST $AZURE_OPENAI_ENDPOINT/openai/deployments/$AZURE_OPENAI_DEPLOYME
                             "chunk_id": "0"
                         }
                     ],
-                    "intent": "[\"Interesting facts\"]"
+                    "intent": "[\"Available health plans\"]"
                 }
             }
         }

articles/ai-services/openai/includes/use-your-data-spring.md

@@ -211,7 +211,7 @@ ai-custom-data-demo/
 
            final var store = vectorStore(embeddingClient);
 
-           final String question = "What are the differences between Azure Machine Learning and Azure AI services?";
+           final String question = "What are my available health plans?";
 
            final var candidateDocs = store.similaritySearch(question);
 
@@ -386,11 +386,7 @@ ai-custom-data-demo/
 Sending custom data prompt to AI service. One moment please...
 
 Prompt created 1 generated response(s).
-Generated response from "assistant": Azure Machine Learning is a cloud-based service that allows users to build, deploy, and manage machine learning models. It provides a range of tools and capabilities for data scientists and developers to train models, automate the machine learning workflow, and deploy models as web services.
-
-On the other hand, Azure AI services is a broader category that includes various services and technologies for artificial intelligence. It encompasses not only machine learning but also other AI capabilities such as natural language processing, computer vision, speech recognition, and more. Azure AI services provide pre-built AI models and APIs that developers can easily integrate into their applications.
-
-In summary, Azure Machine Learning is specifically focused on machine learning model development and deployment, while Azure AI services offer a wider range of AI capabilities beyond just machine learning.
+Generated response from "assistant": The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans.
 ```
 
 > [!div class="nextstepaction"]

articles/ai-services/openai/includes/use-your-data-studio.md

@@ -31,13 +31,11 @@ The **Advanced settings** on the left are [runtime parameters](../concepts/use-y
 - When **Limit responses to your data** is enabled, the model attempts to only rely on your documents for responses. This is set to true by default.
 :::image type="content" source="../media/quickstarts/studio-advanced-settings.png" alt-text="Screenshot of the advanced settings.":::
 
-Send your first query. The chat models perform best in question and answer exercises. For example, "*What was the revenue last year?.*" or "*when was revenue reported?*".
+Send your first query. The chat models perform best in question and answer exercises. For example, "*What are my available health plans?*" or "*What is the health plus option?*".
 
-Queries that require data analysis would probably fail, such as "*which product contributes most to the revenue?*". Queries that require information about all of your data will also likely fail, such as "*how many documents have I uploaded?*". Remember that the search engine looks for chunks having exact or similar terms, phrases, or construction to the query. And while the model might understand the question, if search results are chunks from the data set, it's not the right information to answer that kind of question.
+Queries that require data analysis would probably fail, such as "*Which health plan is most popular?*". Queries that require information about all of your data will also likely fail, such as "*How many documents have I uploaded?*". Remember that the search engine looks for chunks having exact or similar terms, phrases, or construction to the query. And while the model might understand the question, if search results are chunks from the data set, it's not the right information to answer that kind of question.
 
 Chats are constrained by the number of documents (chunks) returned in the response (limited to 3-20 in Azure OpenAI Studio playground). As you can imagine, posing a question about "all of the titles" requires a full scan of the entire vector store.
 
-<!--You can experiment with the configuration settings such as temperature and pre-response text to improve the performance of your task. You can read more about each parameter in the [REST API](../reference.md).-->
-
 [!INCLUDE [deploy-web-app](deploy-web-app.md)]
 

articles/ai-services/openai/use-your-data-quickstart.md

@@ -55,7 +55,7 @@ In this quickstart you can use your own data with Azure OpenAI models. Using Azu
 
 - Be sure that you are assigned at least the [Cognitive Services Contributor](./how-to/role-based-access-control.md#cognitive-services-contributor) role for the Azure OpenAI resource.
 
-- Download the example dataset from [GitHub](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/famous-speeches-pdf).
+- Download the example dataset from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf).
 
 ::: zone pivot="programming-language-javascript"