Acrolinx score updates, standardized access request form language

Author: DenKenMSFT

articles/cognitive-services/openai/concepts/models.md

@@ -64,7 +64,7 @@ While Davinci is the most capable, the other models provide significant speed ad
 
 ### <a id="gpt-3-davinci"></a>Davinci
 
-Davinci is the most capable model and can perform any task the other models can perform, often with less instruction. For applications requiring deep understanding of the content, like summarization for a specific audience and creative content generation, Davinci produces the best results. These increased capabilities require more compute resources, so Davinci costs more and isn't as fast as the other models.
+Davinci is the most capable model and can perform any task the other models can perform, often with less instruction. For applications requiring deep understanding of the content, like summarization for a specific audience and creative content generation, Davinci produces the best results. The increased capabilities provided by Davinci require more compute resources, so Davinci costs more and isn't as fast as the other models.
 
 Another area where Davinci excels is in understanding the intent of text. Davinci is excellent at solving many kinds of logic problems and explaining the motives of characters. Davinci has been able to solve some of the most challenging AI problems involving cause and effect.
 
@@ -103,7 +103,7 @@ Similar to GPT-3, Davinci is the most capable Codex model and can perform any ta
 
 ### Cushman
 
-Cushman is powerful, yet fast. While Davinci is stronger when it comes to analyzing complicated tasks, Cushman is quite capable for many code generation tasks and typically runs faster (and at a lower cost) than Davinci.
+Cushman is powerful, yet fast. While Davinci is stronger when it comes to analyzing complicated tasks, Cushman is capable for many code generation tasks. Cushman typically runs faster and cheaper than Davinci, as well.
 
 ## Embeddings models
 
@@ -116,8 +116,6 @@ Currently, we offer three families of Embeddings models for different functional
 
 Davinci is the most capable, but is slower and more expensive than the other models. Ada is the least capable, but is both faster and cheaper.
 
-These Embeddings models are specifically created to be good at a particular task.
-
 ### Similarity embedding
 
 These models are good at capturing semantic similarity between two or more pieces of text.
@@ -146,7 +144,7 @@ When using our Embeddings models, keep in mind their limitations and risks.
 
 ## Finding the right model
 
-We recommend starting with the most powerful model in a model family, such as the Davinci model, because it's the best way to understand what the service is capable of. After you have an idea of what you want to accomplish, you can either stay with that model or, if balancing capability and cost is a concern for your application, you can move to a model with lower capability and cost, such as Curie or Cushman, and optimize around its capability.
+We recommend starting with the most capable model in a model family because it's the best way to understand what the service is capable of. After you have an idea of what you want to accomplish, you can either stay with that model or move to a model with lower capability and cost, optimizing around that model's capabilities. 
 
 ## Next steps
 

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

@@ -30,9 +30,9 @@ write a tagline for an ice cream shop
 we serve up smiles with every scoop!
 ```
 
-The actual completion results you see may differ because the API is stochastic by default which means that you might get a slightly different completion every time you call it, even if your prompt stays the same. You can control this behavior with the temperature setting.
+The actual completion results you see may differ because the API is stochastic by default. In other words, you might get a slightly different completion every time you call it, even if your prompt stays the same. You can control this behavior with the temperature setting.
 
-This simple text-in, text-out interface means you can "program" the model by providing instructions or just a few examples of what you'd like it to do. Its 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 middle school student to solve. A well-written prompt provides enough information for the model to know what you want and how it should respond.
+This simple, "text in, text out" interface means you can "program" the model by providing instructions or just a few examples of what you'd like it to do. Its 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 middle school student to solve. A well-written prompt provides enough information for the model to know what you want and how it should respond.
 
 > [!NOTE]
 > Keep in mind that the models' training data cuts off in October 2019, so they may not have knowledge of current events. We plan to add more continuous training in the future.
@@ -43,15 +43,15 @@ This simple text-in, text-out interface means you can "program" the model by pro
 
 OpenAI's models can do everything from generating original stories to performing complex text analysis. Because they can do so many things, you have to be explicit in showing what you want. Showing, not just telling, is often the secret to a good prompt. 
 
-The models try to predict what you want from the prompt. If you send the words "Give me a list of cat breeds," the model wouldn't automatically assume that you're asking for a list of cat breeds. You could just as easily be asking the model to continue a conversation where the first words are "Give me a list of cat breeds" and the next ones are "and I'll tell you which ones I like." If the model only assumed that you wanted a list of cats, it wouldn't be as good at content creation, classification, or other tasks.
+The models try to predict what you want from the prompt. If you send the words "Give me a list of cat breeds," the model wouldn't automatically assume that you're asking for a list of cat breeds. You could as easily be asking the model to continue a conversation where the first words are "Give me a list of cat breeds" and the next ones are "and I'll tell you which ones I like." If the model only assumed that you wanted a list of cats, it wouldn't be as good at content creation, classification, or other tasks.
 
 There are three basic guidelines to creating prompts:
 
 **Show and tell.** Make it clear what you want either through instructions, examples, or a combination of the two. If you want the model to rank a list of items in alphabetical order or to classify a paragraph by sentiment, show it that's what you want.
 
-**Provide quality data.** If you're trying to build a classifier or get the model to follow a pattern, make sure that there are enough examples. Be sure to proofread your examples — the model is usually smart enough to see through basic spelling mistakes and give you a response, but it also might assume this is intentional and it can affect the response.
+**Provide quality data.** If you're trying to build a classifier or get the model to follow a pattern, make sure that there are enough examples. Be sure to proofread your examples — the model is usually smart enough to see through basic spelling mistakes and give you a response, but it also might assume that the mistakes are intentional and it can affect the response.
 
-**Check your settings.** The temperature and top_p settings control how deterministic the model is in generating a response. If you're asking it for a response where there's only one right answer, then you'd want to set these lower. If you're looking for a response that's not obvious, then you might want to set them higher. The number one mistake people use with these settings is assuming that they're "cleverness" or "creativity" controls.
+**Check your settings.** The temperature and top_p settings control how deterministic the model is in generating a response. If you're asking it for a response where there's only one right answer, then you'd want to set these settings to lower values. If you're looking for a response that's not obvious, then you might want to set them to higher values. The number one mistake people use with these settings is assuming that they're "cleverness" or "creativity" controls.
 
 ### Troubleshooting
 
@@ -485,7 +485,7 @@ Create an array of users and email addresses
 """
 ```
 
-**Put comments inside of functions can be helpful.** Recommended coding standards usually suggest placing the description of a function inside the function. Using this format helps Codex more clearly understand what you want the function to do.
+**Put comments inside of functions can be helpful.** Recommended coding standards suggest placing the description of a function inside the function. Using this format helps Codex more clearly understand what you want the function to do.
 
 ```
 def getUserBalance(id):
@@ -558,7 +558,7 @@ Use the lists to generate stories about what I saw at the zoo in each city
 */
 ```
 
-**Use Codex to explain code.** Codex's ability to create and understand code allows us to use it to perform tasks like explaining what the code in a file does. One way to accomplish this is by putting a comment after a function that starts with "This function" or "This application is."  Codex will usually interpret this as the start of an explanation and complete the rest of the text.
+**Use Codex to explain code.** Codex's ability to create and understand code allows us to use it to perform tasks like explaining what the code in a file does. One way to accomplish this is by putting a comment after a function that starts with "This function" or "This application is."  Codex typically interprets this comment as the start of an explanation and completes the rest of the text.
 
 ```
 /* Explain what the previous function is doing: It

articles/cognitive-services/openai/how-to/fine-tuning.md

@@ -23,7 +23,8 @@ The Azure OpenAI Service lets you tailor our models to your personal datasets us
 ## Prerequisites
 
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
-- Access granted to service in the desired Azure subscription. This service is currently invite only. You can fill out a new use case request here: <https://aka.ms/oai/access>. Please open an issue on this repo to contact us if you have an issue
+- Access granted to the service in the desired Azure subscription
+  Currently, access to this service is granted only by invitation. You can request access to the Azure OpenAI service by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.
 - The following Python libraries: os, requests, json
 - An Azure OpenAI Service resource with a model deployed. If you don't have a resource/model the process is documented in our [resource deployment guide](../how-to/create-resource.md)
 

articles/cognitive-services/openai/how-to/managed-identity.md

@@ -21,7 +21,8 @@ In the following sections, you'll use  the Azure CLI to assign roles, and obtain
 ## Prerequisites
 
 - An Azure subscription
-- Access granted to service in the desired Azure subscription. 
+- Access granted to the service in the desired Azure subscription
+  Currently, access to this service is granted only by invitation. You can request access to the Azure OpenAI service by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.
 - Azure CLI. [Installation Guide](/cli/azure/install-azure-cli)
 - The following Python libraries: os, requests, json
 

articles/cognitive-services/openai/includes/create-resource-cli.md

@@ -15,7 +15,8 @@ keywords:
 ## Prerequisites
 
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
-- Access granted to service in the desired AzuFre subscription. This service is currently invite only. You can fill out a new use case request here: <https://aka.ms/oai/access>. Please open an issue on this repo to contact us if you have an issue
+- Access granted to the service in the desired Azure subscription
+  Currently, access to this service is granted only by invitation. You can request access to the Azure OpenAI service by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.
 - Azure CLI. [Installation guide](/cli/azure/install-azure-cli)
 
 ## Sign in to the CLI

articles/cognitive-services/openai/includes/create-resource-portal.md

@@ -14,7 +14,8 @@ keywords:
 ## Prerequisites
 
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
-- Access granted to the service in the desired Azure subscription. This service is currently invite only. You can fill out a new use case request here: <https://aka.ms/oai/access>.
+- Access granted to the service in the desired Azure subscription
+  Currently, access to this service is granted only by invitation. You can request access to the Azure OpenAI service by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.
 
 ## Create a resource
 

articles/cognitive-services/openai/includes/python.md

@@ -16,7 +16,8 @@ keywords:
 ## Prerequisites
 
 - An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>
-- Access granted to service in the desired Azure subscription. This service is currently invite only. You can fill out a new use case request here:<a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>
+- Access granted to the service in the desired Azure subscription
+  Currently, access to this service is granted only by invitation. You can request access to the Azure OpenAI service by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.
 - <a href="https://www.python.org/" target="_blank">Python 3.7.1 or later version</a>
 - The following Python libraries: os, requests, json
 - An Azure OpenAI Service resource with a model deployed. If you don't have a resource/model the process is documented in our [resource deployment guide](../how-to/create-resource.md)

articles/cognitive-services/openai/includes/rest.md

@@ -14,7 +14,8 @@ keywords:
 ## Prerequisites
 
 - An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>
-- Access granted to service in the desired Azure subscription. This service is currently invite only. You can fill out a new use case request here:<a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>
+- Access granted to the service in the desired Azure subscription
+  Currently, access to this service is granted only by invitation. You can request access to the Azure OpenAI service by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.
 - <a href="https://www.python.org/" target="_blank">Python 3.7.1 or later version</a>
 - The following Python libraries: os, requests, json
 - An Azure OpenAI Service resource with a model deployed. If you don't have a resource/model the process is documented in our [resource deployment guide](../how-to/create-resource.md)

articles/cognitive-services/openai/includes/studio.md

@@ -14,7 +14,8 @@ keywords:
 ## Prerequisites
 
 - An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>
-- Access granted to service in the desired Azure subscription. Currently, this service is available only by invitation. You can fill out a new use case request here: <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>
+- Access granted to the service in the desired Azure subscription
+  Currently, access to this service is granted only by invitation. You can request access to the Azure OpenAI service by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.
 - An Azure OpenAI Service resource with a model deployed. If you don't have a resource/model, the process is documented in our [resource deployment guide](../how-to/create-resource.md)
 
 ## Go to the Azure OpenAI Studio