update

Author: mrbullwinkle

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

@@ -1,12 +1,12 @@
 ---
 title: Design system messages with Azure OpenAI
 titleSuffix: Azure OpenAI Service
-description: Learn about system message design with GPT-3, GPT-35-Turbo, and GPT-4 models.
+description: Learn about system message design
 author: mrbullwinkle
 ms.author: mbullwin 
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 09/05/2024
+ms.date: 03/26/2025
 manager: nitinme
 keywords: ChatGPT, GPT-4, meta prompts, chain of thought
 ---
@@ -17,9 +17,9 @@ This guide will walk you through some techniques in system message design.
 
 
 
-## What is a system message? 
+## What is a system message?
 
-A system message is a feature-specific set of instructions or contextual frameworks given to a generative AI model (e.g. GPT4-o, GPT3.5 Turbo, etc.) to direct and improve the quality and safety of a model’s output. This is particularly helpful in situations that need certain degrees of formality, technical language, or industry-specific terms.  
+A system message is a feature-specific set of instructions or contextual frameworks given to a generative AI model (e.g. GPT-4o, GPT-3.5 Turbo, etc.) to direct and improve the quality and safety of a model’s output. This is particularly helpful in situations that need certain degrees of formality, technical language, or industry-specific terms.  
 
 
 There is no prescribed length. A system message can be one short sentence:  

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

@@ -5,7 +5,7 @@ description: Learn about performance and latency with Azure OpenAI
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 09/20/2024
+ms.date: 03/26/2025
 author: mrbullwinkle 
 ms.author: mbullwin
 recommendations: false

articles/ai-services/openai/how-to/provisioned-get-started.md

@@ -8,7 +8,7 @@ ms.custom: openai
 ms.topic: how-to
 author: mrbullwinkle
 ms.author: mbullwin
-ms.date: 09/20/2024
+ms.date: 03/26/2025
 recommendations: false
 ---
 
@@ -136,14 +136,13 @@ The inferencing code for provisioned deployments is the same a standard deployme
 
 
 ```python
-    #Note: The openai-python library support for Azure OpenAI is in preview. 
     import os
     from openai import AzureOpenAI
 
     client = AzureOpenAI(
         azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
         api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
-        api_version="2024-02-01"
+        api_version="2024-10-21"
     )
 
     response = client.chat.completions.create(
@@ -203,7 +202,7 @@ from openai import AzureOpenAI
 client = AzureOpenAI(
     azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"),
     api_key=os.getenv("AZURE_OPENAI_API_KEY"),
-    api_version="2024-02-01",
+    api_version="2024-10-21",
     max_retries=5,# default is 2
 )
 

articles/ai-services/openai/how-to/reproducible-output.md

@@ -6,7 +6,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 09/20/2024
+ms.date: 03/26/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -50,7 +50,7 @@ from openai import AzureOpenAI
 client = AzureOpenAI(
   azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
   api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
-  api_version="2024-02-01"
+  api_version="2024-10-21"
 )
 
 for i in range(3):
@@ -79,7 +79,7 @@ for i in range(3):
 $openai = @{
    api_key     = $Env:AZURE_OPENAI_API_KEY
    api_base    = $Env:AZURE_OPENAI_ENDPOINT # like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
-   api_version = '2024-02-01' # may change in the future
+   api_version = '2024-10-21' # may change in the future
    name        = 'YOUR-DEPLOYMENT-NAME-HERE' # name you chose for your deployment
 }
 
@@ -145,7 +145,7 @@ from openai import AzureOpenAI
 client = AzureOpenAI(
   azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
   api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
-  api_version="2024-02-01"
+  api_version="2024-10-21"
 )
 
 for i in range(3):
@@ -174,7 +174,7 @@ for i in range(3):
 $openai = @{
    api_key     = $Env:AZURE_OPENAI_API_KEY
    api_base    = $Env:AZURE_OPENAI_ENDPOINT # like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
-   api_version = '2024-02-01' # may change in the future
+   api_version = '2024-10-21' # may change in the future
    name        = 'YOUR-DEPLOYMENT-NAME-HERE' # name you chose for your deployment
 }
 

articles/ai-services/openai/how-to/work-with-code.md

@@ -5,17 +5,17 @@ description: Learn how to use the Codex models on Azure OpenAI to handle a varie
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 09/20/2024
+ms.date: 03/26/2025
 author: mrbullwinkle
 ms.author: mbullwin
 ---
 
-# Codex models and Azure OpenAI Service
+# Completion models and code with Azure OpenAI Service
 
 > [!IMPORTANT]
-> This article was authored and tested against the [legacy code generation models](/azure/ai-services/openai/concepts/legacy-models). These models use the completions API, and its prompt/completion style of interaction. If you wish to test the techniques described in this article verbatim we recommend using the `gpt-35-turbo-instruct` model which allows access to the completions API. However, for code generation the chat completions API and the latest GPT-4o models will yield the best results, but the prompts would need to be converted to the conversational style specific to interacting with those models.
+> This article was authored and tested against the [legacy code generation models](/azure/ai-services/openai/concepts/legacy-models). These models use the completions API, and its prompt/completion style of interaction. If you wish to test the techniques described in this article verbatim we recommend using the `gpt-35-turbo-instruct` model which allows access to the completions API. However, for code generation the chat completions API and the latest GPT-4o and o-series models will yield the best results. To use these newer models these prompts would need to be converted to the conversational style specific to interacting with those models.
 
-The Codex model series is a descendant of our GPT-3 series that's been trained on both natural language and billions of lines of code. It's most capable in Python and proficient in over a dozen languages including C#, JavaScript, Go, Perl, PHP, Ruby, Swift, TypeScript, SQL, and even Shell.
+The Codex model series was a descendant of our GPT-3 series that's been trained on both natural language and billions of lines of code. It's most capable in Python and proficient in over a dozen languages including C#, JavaScript, Go, Perl, PHP, Ruby, Swift, TypeScript, SQL, and even Shell.
 
 You can use Codex for a variety of tasks including:
 
@@ -27,7 +27,7 @@ You can use Codex for a variety of tasks including:
 
 ## How to use completions models with code
 
-Here are a few examples of using Codex that can be tested in the [Azure AI Foundry](https://ai.azure.com) playground with a deployment of a Codex series model, such as `code-davinci-002`.
+Here are a few examples of using completion models that can be tested in the [Azure AI Foundry](https://ai.azure.com) playground with a deployment of `gpt-35-turbo-instruct`.
 
 ### Saying "Hello" (Python)
 

articles/ai-services/openai/includes/fine-tune.md

@@ -6,7 +6,7 @@ author: mrbullwinkle
 ms.author: mbullwin 
 ms.service: azure-ai-openai
 ms.topic: include
-ms.date: 09/01/2023
+ms.date: 03/26/2025
 manager: nitinme
 keywords: ChatGPT
 

articles/ai-services/openai/tutorials/fine-tune.md

@@ -5,7 +5,7 @@ description: Learn how to use Azure OpenAI's latest fine-tuning capabilities wit
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: tutorial
-ms.date: 09/09/2024
+ms.date: 03/26/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false