Merge branch 'sora-build' of https://github.com/PatrickFarley/azure-ai-docs-pr into sora-build

Author: PatrickFarley

articles/ai-services/openai/concepts/video-generation.md

@@ -47,5 +47,5 @@ A user can have two video creation jobs running at the same time. In that situat
 
 Sora has a robust safety stack including content filtering, abuse monitoring, sensitive content blocking, and safety classifiers.
 
-Sora does not generate scenes with acts of violence but can generate adjacent content, such as realistic war-like footage.
+Sora doesn't generate scenes with acts of violence but can generate adjacent content, such as realistic war-like footage.
 

articles/ai-services/openai/whats-new.md

@@ -153,7 +153,7 @@ For more information, see the [GPT-4o real-time audio quickstart](realtime-audio
 
 ### o1 reasoning model released for limited access
 
-The latest `o1` model is now available for API access and model deployment. **Registration is required, and access will be granted based on Microsoft's eligibility criteria**. Customers who previously applied and received access to `o1-preview`, don't need to reapply as they are automatically on the wait-list for the latest model.
+The latest `o1` model is now available for API access and model deployment. **Registration is required, and access will be granted based on Microsoft's eligibility criteria**. Customers who previously applied and received access to `o1-preview`, don't need to reapply as they're automatically on the wait-list for the latest model.
 
 Request access: [limited access model application](https://aka.ms/OAI/o1access)
 
@@ -167,7 +167,7 @@ To learn more about the advanced `o1` series models see, [getting started with o
 
 ### Preference fine-tuning (preview)
 
-[Direct preference optimization (DPO)](./how-to/fine-tuning-direct-preference-optimization.md) is a new alignment technique for large language models, designed to adjust model weights based on human preferences. Unlike reinforcement learning from human feedback (RLHF), DPO does not require fitting a reward model and uses simpler data (binary preferences) for training. This method is computationally lighter and faster, making it equally effective at alignment while being more efficient. DPO is especially useful in scenarios where subjective elements like tone, style, or specific content preferences are important. We’re excited to announce the public preview of DPO in Azure OpenAI, starting with the `gpt-4o-2024-08-06` model.
+[Direct preference optimization (DPO)](./how-to/fine-tuning-direct-preference-optimization.md) is a new alignment technique for large language models, designed to adjust model weights based on human preferences. Unlike reinforcement learning from human feedback (RLHF), DPO doesn't require fitting a reward model and uses simpler data (binary preferences) for training. This method is computationally lighter and faster, making it equally effective at alignment while being more efficient. DPO is especially useful in scenarios where subjective elements like tone, style, or specific content preferences are important. We’re excited to announce the public preview of DPO in Azure OpenAI, starting with the `gpt-4o-2024-08-06` model.
 
 For fine-tuning model region availability, see the [models page](./concepts/models.md#fine-tuning-models).
 
@@ -205,7 +205,7 @@ For fine-tuning model region availability, see the [models page](./concepts/mode
 
 ### NEW AI abuse monitoring
 
-We are introducing new forms of abuse monitoring that leverage LLMs to improve efficiency of detection of potentially abusive use of the Azure OpenAI and to enable abuse monitoring without the need for human review of prompts and completions. Learn more, see [Abuse monitoring](/azure/ai-services/openai/concepts/abuse-monitoring).
+We're introducing new forms of abuse monitoring that leverage LLMs to improve efficiency of detection of potentially abusive use of the Azure OpenAI and to enable abuse monitoring without the need for human review of prompts and completions. Learn more, see [Abuse monitoring](/azure/ai-services/openai/concepts/abuse-monitoring).
 
 Prompts and completions that are flagged through content classification and/or identified to be part of a potentially abusive pattern of use are subjected to an additional review process to help confirm the system's analysis and inform actioning decisions. Our abuse monitoring systems have been expanded to enable review by LLM by default and by humans when necessary and appropriate. 
 
@@ -318,13 +318,13 @@ OpenAI has incorporated additional safety measures into the `o1` models, includi
 
 ### Availability
 
-The `o1-preview` and `o1-mini` are available in the East US2 region for limited access through the [Azure AI Foundry portal](https://ai.azure.com) early access playground. Data processing for the `o1` models might occur in a different region than where they are available for use.
+The `o1-preview` and `o1-mini` are available in the East US2 region for limited access through the [Azure AI Foundry portal](https://ai.azure.com) early access playground. Data processing for the `o1` models might occur in a different region than where they're available for use.
 
 To try the `o1-preview` and `o1-mini` models in the early access playground **registration is required, and access will be granted based on Microsoft’s eligibility criteria.**
 
 Request access: [limited access model application](https://aka.ms/oai/modelaccess)
 
-Once access has been granted, you will need to:
+Once access has been granted, you'll need to:
 
 1. Navigate to https://ai.azure.com/resources and select a resource in the `eastus2` region. If you don't have an Azure OpenAI resource in this region you'll need to [create one](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI).  
 2. Once the `eastus2` Azure OpenAI resource is selected, in the upper left-hand panel under **Playgrounds** select **Early access playground (preview)**.
@@ -381,7 +381,7 @@ Unlike the previous early access playground, the [Azure AI Foundry portal](https
 > [!NOTE]
 > Prompts and completions made through the early access playground (preview) might be processed in any Azure OpenAI region, and are currently subject to a 10 request per minute per Azure subscription limit. This limit might change in the future.
 >
-> Azure OpenAI abuse monitoring is enabled for all early access playground users even if approved for modification; default content filters are enabled and cannot be modified.
+> Azure OpenAI abuse monitoring is enabled for all early access playground users even if approved for modification; default content filters are enabled and can't be modified.
 
 To test out GPT-4o `2024-08-06`, sign-in to the Azure AI early access playground (preview) using this [link](https://aka.ms/oai/docs/earlyaccessplayground).