correctness fixes

Author: PatrickFarley

articles/ai-services/openai/concepts/gpt-with-vision.md

@@ -28,7 +28,7 @@ Enhancements let you incorporate other Azure AI services (such as Azure AI Visio
 
 **Object grounding**: Azure AI Vision complements GPT-4 Turbo with Vision’s text response by identifying and locating salient objects in the input images. This lets the chat model give more accurate and detailed responses about the contents of the image.
 
-:::image type="content" source="../media/concepts/gpt-v/object-grounding.png" alt-text="Screenshot of an image with object grounding applied. Object have bounding boxes with labels.":::
+:::image type="content" source="../media/concepts/gpt-v/object-grounding.png" alt-text="Screenshot of an image with object grounding applied. Objects have bounding boxes with labels.":::
 
 :::image type="content" source="../media/concepts/gpt-v/object-grounding-response.png" alt-text="Screenshot of a chat response to an image prompt about an outfit. The response is an itemized list of clothing items seen in the image.":::
 
@@ -40,7 +40,7 @@ Enhancements let you incorporate other Azure AI services (such as Azure AI Visio
 
 **Video prompt**: The **video prompt** enhancement lets you use video clips as input for AI chat, enabling the model to generate summaries and answers about video content. It uses Azure AI Vision Video Retrieval to sample a set of frames from a video and create a transcript of the speech in the video.
 
-In order to use the video prompt enhancement, you need both an Azure AI Vison resource and an Azure Video Indexer resource, in addition to your Azure OpenAI resource.
+In order to use the video prompt enhancement, you need both an Azure AI Vision resource and an Azure Video Indexer resource, in addition to your Azure OpenAI resource.
 
 > [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RW1eHRf]
 
@@ -63,6 +63,9 @@ Processing videos involves the use of extra tokens to identify key frames for an
 
 ### Example price calculation
 
+> [!IMPORTANT]
+> The following content is an example only, and prices are subject to change in the future.
+
 For a typical use case, take a 3-minute video with a 100-token prompt input. The video has a transcript that's 100 tokens long, and when the service processes the prompt, it generates 100 tokens of output. The pricing for this transaction would be:
 
 | Item        | Detail        | Total Cost   |
@@ -73,7 +76,7 @@ For a typical use case, take a 3-minute video with a 100-token prompt input. The
 | Output Tokens      | 100 tokens (assumed)    | $0.003       |
 | **Total Cost**      |      | **$0.03025** |
 
-Additionally, there's a one-time indexing cost of $0.15 to generate the Video Retrieval index for this 3-minute video. This index can be reused across any number of Video Retrieval and GPT-4 Turbo with Vision calls.
+Additionally, there's a one-time indexing cost of $0.15 to generate the Video Retrieval index for this 3-minute video. This index can be reused across any number of Video Retrieval and GPT-4 Turbo with Vision API calls.
 
 ## Limitations
 
@@ -85,7 +88,7 @@ This section describes the limitations of GPT-4 Turbo with Vision.
 - **Maximum input image size**: The maximum size for input images is restricted to 20 MB.
 - **Object grounding in enhancement API**: When the enhancement API is used for object grounding, and the model detects duplicates of an object, it will generate one bounding box and label for all the duplicates instead of separate ones for each.
 - **Low resolution accuracy**: When images are analyzed using the "low resolution" setting, it allows for faster responses and uses fewer input tokens for certain use cases. However, this could impact the accuracy of object and text recognition within the image.
-- **Image chat restriction**: When uploading images in Azure OpenAI Studio or the API, there is a limit of 10 images per chat call.
+- **Image chat restriction**: When you upload images in Azure OpenAI Studio or the API, there is a limit of 10 images per chat call.
 
 ### Video support
 

articles/ai-services/openai/faq.yml

@@ -92,13 +92,13 @@ sections:
           To learn more about how GPT models are trained and work we recommend watching [Andrej Karpathy's talk from Build 2023 on the state of GPT](https://www.youtube.com/watch?v=bZQun8Y4L2A).
       
       - question: |
-          I asked the model when it's knowledge cutoff is and it gave me a different answer than what is on the Azure OpenAI model's page. Why does this happen?
+          I asked the model when its knowledge cutoff is and it gave me a different answer than what is on the Azure OpenAI model's page. Why does this happen?
         answer:
           This is expected behavior. The models aren't able to answer questions about themselves. If you want to know when the knowledge cutoff for the model's training data is, consult the [models page](./concepts/models.md).
       - question: |
           I asked the model a question about something that happened recently before the knowledge cutoff and it got the answer wrong. Why does this happen?
         answer: |
-          This is expected behavior. First there's no guarantee that every recent event that has occurred was part of the model's training data. And even when information was part of the training data, without using additional techniques like Retrieval Augmented Generation (RAG) to help ground the model's responses there's always a chance of ungrounded responses occurring. Both Azure OpenAI's [use your data feature](./concepts/use-your-data.md) and [Bing Chat](https://www.microsoft.com/edge/features/bing-chat?form=MT00D8) use Azure OpenAI models combined with Retrieval Augmented Generation to help further ground model responses. 
+          This is expected behavior. First there's no guarantee that every recent event was part of the model's training data. And even when information was part of the training data, without using additional techniques like Retrieval Augmented Generation (RAG) to help ground the model's responses there's always a chance of ungrounded responses occurring. Both Azure OpenAI's [use your data feature](./concepts/use-your-data.md) and [Bing Chat](https://www.microsoft.com/edge/features/bing-chat?form=MT00D8) use Azure OpenAI models combined with Retrieval Augmented Generation to help further ground model responses. 
           
           The frequency that a given piece of information appeared in the training data can also impact the likelihood that the model will respond in a certain way. 
 

articles/ai-services/openai/how-to/gpt-with-vision.md

@@ -175,7 +175,7 @@ Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployme
 
 The format is similar to that of the chat completions API for GPT-4, but the message content can be an array containing strings and images (either a valid HTTP or HTTPS URL to an image, or a base-64-encoded image).
 
-You must also include the `enhancements` and `dataSources` objects. `enhancements` represents the specific Vision enhancement features requested in the chat. It has a `grounding` and `ocr` property, which each have a boolean `enabled` property. Use these to request the OCR service and/or the object detection/grounding service. `dataSources` represents the Computer Vision resource data that's needed for Vision enhancement. It has a `type` property which should be `"AzureComputerVision"` and a `parameters` property. Set the `endpoint` and `key` to the endpoint URL and access key of your Computer Vision resource. Remember to set a `"max_tokens"` value, or the return output will be cut off.
+You must also include the `enhancements` and `dataSources` objects. `enhancements` represents the specific Vision enhancement features requested in the chat. It has a `grounding` and `ocr` property, which both have a boolean `enabled` property. Use these to request the OCR service and/or the object detection/grounding service. `dataSources` represents the Computer Vision resource data that's needed for Vision enhancement. It has a `type` property which should be `"AzureComputerVision"` and a `parameters` property. Set the `endpoint` and `key` to the endpoint URL and access key of your Computer Vision resource. Remember to set a `"max_tokens"` value, or the return output will be cut off.
 
 ```json
 {