update

Author: mrbullwinkle

articles/ai-services/openai/concepts/fine-tuning-considerations.md

@@ -4,7 +4,7 @@ description: Learn more about what you should take into consideration before fin
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 08/29/2024
+ms.date: 03/26/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false

articles/ai-services/openai/concepts/prompt-engineering.md

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn how to use prompt engineering to optimize your work with Azure OpenAI Service.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 08/29/2024
+ms.date: 03/26/2025
 ms.custom: references_regions, build-2023, build-2023-dataai
 manager: nitinme
 author: mrbullwinkle
@@ -14,7 +14,7 @@ recommendations: false
 
 # Prompt engineering techniques
 
-GPT-3, GPT-3.5, GPT-4, and GPT-4o models from OpenAI are prompt-based. With prompt-based models, the user interacts with the model by entering a text prompt, to which the model responds with a text completion. This completion is the model’s continuation of the input text.
+GPT-3, GPT-3.5, GPT-4, and GPT-4o models from OpenAI are prompt-based. With prompt-based models, the user interacts with the model by entering a text prompt, to which the model responds with a text completion. This completion is the model’s continuation of the input text. These techniques are note recommended for o-series models.
 
 While these models are extremely powerful, their behavior is also very sensitive to the prompt. This makes prompt construction an important skill to develop.
 

articles/ai-services/openai/concepts/provisioned-throughput.md

@@ -3,7 +3,7 @@ title: Azure OpenAI Service provisioned throughput
 description: Learn about provisioned throughput and Azure OpenAI.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 08/07/2024
+ms.date: 03/26/2025
 manager: nitinme
 author: mrbullwinkle #ChrisHMSFT
 ms.author: mbullwin #chrhoder

articles/ai-services/openai/how-to/completions.md

@@ -5,7 +5,7 @@ description: Learn how to generate or manipulate text, including code by using t
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 08/29/2024
+ms.date: 03/26/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -17,7 +17,7 @@ recommendations: false
 Azure OpenAI Service provides a **completion endpoint** that can be used for a wide variety of tasks. The endpoint supplies a simple yet powerful text-in, text-out interface to any [Azure OpenAI model](../concepts/models.md). To trigger the completion, you input some text as a prompt. The model generates the completion and attempts to match your context or pattern. Suppose you provide the prompt "As Descartes said, I think, therefore" to the API. For this prompt, Azure OpenAI returns the completion endpoint " I am" with high probability.
 
 > [!IMPORTANT]
-> Unless you have a specific use case that requires the completions endpoint, we recommend instead using the [chat completions endpoint](./chatgpt.md) which allows you to take advantage of the latest models like GPT-4o, GPT-4o mini, and GPT-4 Turbo. 
+> Unless you have a specific use case that requires the completions endpoint, we recommend instead using the [responses API](./responses.md) of [chat completions endpoint](./chatgpt.md) which allows you to take advantage of the latest models like GPT-4o, GPT-4o mini, and GPT-4 Turbo. 
 
 The best way to start exploring completions is through the playground in [Azure AI Foundry](https://ai.azure.com). It's a simple text box where you enter a prompt to generate a completion. You can start with a simple prompt like this one:
 
@@ -36,7 +36,7 @@ The completion results that you see can differ because the Azure OpenAI API prod
 The simple text-in, text-out interface means you can "program" the Azure OpenAI model by providing instructions or just a few examples of what you'd like it to do. The output success generally depends on the complexity of the task and quality of your prompt. A general rule is to think about how you would write a word problem for a pre-teenage student to solve. A well-written prompt provides enough information for the model to know what you want and how it should respond.
 
 > [!NOTE]
-> The model training data can be different for each model type. The [latest model's training data currently extends through September 2021 only](/azure/ai-services/openai/concepts/models). Depending on your prompt, the model might not have knowledge of related current events.
+> The model training data can be different for each model type. Depending on your prompt, the model might not have knowledge of related current events.
 
 ## Design prompts
 

articles/ai-services/openai/how-to/embeddings.md

@@ -6,7 +6,7 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.custom: devx-track-python
 ms.topic: how-to
-ms.date: 08/29/2024
+ms.date: 03/26/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -21,7 +21,7 @@ To obtain an embedding vector for a piece of text, we make a request to the embe
 
 # [console](#tab/console)
 ```console
-curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/embeddings?api-version=2024-02-01\
+curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/deployments/YOUR_DEPLOYMENT_NAME/embeddings?api-version=2024-10-21\
   -H 'Content-Type: application/json' \
   -H 'api-key: YOUR_API_KEY' \
   -d '{"input": "Sample Document goes here"}'
@@ -35,7 +35,7 @@ from openai import AzureOpenAI
 
 client = AzureOpenAI(
   api_key = os.getenv("AZURE_OPENAI_API_KEY"),  
-  api_version = "2024-06-01",
+  api_version = "2024-10-21",
   azure_endpoint =os.getenv("AZURE_OPENAI_ENDPOINT") 
 )
 
@@ -47,26 +47,6 @@ response = client.embeddings.create(
 print(response.model_dump_json(indent=2))
 ```
 
-# [OpenAI Python 0.28.1](#tab/python)
-
-[!INCLUDE [Deprecation](../includes/deprecation.md)]
-
-```python
-import openai
-
-openai.api_type = "azure"
-openai.api_key = "YOUR_API_KEY"
-openai.api_base = "https://YOUR_RESOURCE_NAME.openai.azure.com"
-openai.api_version = "2024-06-01"
-
-response = openai.Embedding.create(
-    input="Your text string goes here",
-    engine="YOUR_DEPLOYMENT_NAME"
-)
-embeddings = response['data'][0]['embedding']
-print(embeddings)
-```
-
 # [C#](#tab/csharp)
 ```csharp
 using Azure;

articles/ai-services/openai/how-to/json-mode.md

@@ -6,7 +6,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 08/29/2024
+ms.date: 03/26/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -51,7 +51,7 @@ from openai import AzureOpenAI
 client = AzureOpenAI(
   azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
   api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
-  api_version="2024-03-01-preview"
+  api_version="2025-03-01-preview"
 )
 
 response = client.chat.completions.create(

articles/ai-services/openai/how-to/monitor-openai.md

@@ -1,7 +1,7 @@
 ---
 title: Monitor Azure OpenAI Service
 description: Start here to learn how to use Azure Monitor tools like Log Analytics to capture and analyze metrics and data logs for your Azure OpenAI Service.
-ms.date: 08/20/2024
+ms.date: 03/26/2025
 ms.custom: horz-monitor, subject-monitoring
 ms.topic: conceptual
 author: mrbullwinkle

articles/ai-services/openai/how-to/working-with-models.md

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn about managing model deployment life cycle, updates, & retirement.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 08/29/2024
+ms.date: 03/26/2025
 ms.custom: references_regions, build-2023, build-2023-dataai, devx-track-azurepowershell
 manager: nitinme
 author: mrbullwinkle #ChrisHMSFT