Acrolinx suggestions

Author: ginalee-dotcom

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

@@ -50,7 +50,7 @@ Azure OpenAI evaluation enables developers to create evaluation runs to test aga
 - West US 2
 - West US 3
 
-If your preferred region is missing, please refer to [Azure OpenAI regions](https://learn.microsoft.com/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#global-standard-model-availability) and check if it is one of the Azure OpenAI regional availability zones.
+If your preferred region is missing, refer to [Azure OpenAI regions](https://learn.microsoft.com/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#global-standard-model-availability) and check if it is one of the Azure OpenAI regional availability zones.
 
 ### Supported deployment types
 
@@ -63,7 +63,7 @@ If your preferred region is missing, please refer to [Azure OpenAI regions](http
 
 ## Evaluation API (preview)
 
-Evaluation API lets users test and improve model outputs directly through API calls, making the experience simple and customizable for developers to programmatically assess model quality and performance in their development workflows. To use Evaluation API, check out the [REST API documentation](https://learn.microsoft.com/azure/ai-services/openai/authoring-reference-preview#evaluation---get-list). 
+Evaluation API lets you test model outputs directly through API calls, and programmatically assess model quality and performance. To use Evaluation API, check out the [REST API documentation](https://learn.microsoft.com/azure/ai-services/openai/authoring-reference-preview#evaluation---get-list). 
 
 ## Evaluation pipeline
 
@@ -119,11 +119,9 @@ Outputs generated during the evaluation will be referenced in subsequent steps u
 
 ### Model deployment
 
-As part of creating evaluations you'll pick which models to use when generating responses (optional) as well as which models to use when grading models with specific testing criteria.  
+In Azure OpenAI, you need to create a model deployment to use for your evaluation. You can pick and deploy a single model, or multiple models, depending on your needs. These model deployments will be used when grading your base model or your fine-tuned model with the test criteria of your choice. You can also use the deployed models to auto-generate responses for your provided prompt. 
 
-In Azure OpenAI you'll be assigning specific model deployments to use as part of your evaluations. You can compare multiple model deployments in single evaluation run.
-
-You can evaluate either base or fine-tuned model deployments. The deployments available in your list depend on those you created within your Azure OpenAI resource. If you can't find the desired deployment, you can create a new one from the Azure OpenAI Evaluation page.
+The deployments available in your list depend on those you created within your Azure OpenAI resource. If you can't find the desired deployment, you can create a new one from the Azure OpenAI Evaluation page.
 
 ### Testing criteria
 
@@ -177,11 +175,11 @@ You will select the model of your choice. If you do not have a model, you can cr
 
 :::image type="content" source="../media/how-to/evaluations/eval-generate-2.png" alt-text="Screenshot of the UX for generating model responses" lightbox="../media/how-to/evaluations/eval-generate-2.png":::
 
-6. For creating a test criteria, select **Add**. For the example file we provided above, we are going to be assessing semantic similarity. Select **Model Scorer**, which contains test criteria presets for Semantic Similarity.
+6. For creating a test criteria, select **Add**. For the example file we provided, we are going to be assessing semantic similarity. Select **Model Scorer**, which contains test criteria presets for Semantic Similarity.
 
     :::image type="content" source="../media/how-to/evaluations/eval-semantic-similarity-1.png" alt-text="Screenshot of the semantic similarity UX config." lightbox="../media/how-to/evaluations/eval-semantic-similarity-1.png":::
 
-   Select **Semantic Similarity** at the top. Scroll to the bottom, and in `User` section, specify `{{item.output}}` as `Ground truth`, and specify `{{sample.output_text}}` as `Output`. This will take the original reference output from your evaluation `.jsonl` file (the sample file above) and compare it against the output that is generated by the model you chose in the previous step.
+   Select **Semantic Similarity** at the top. Scroll to the bottom, and in `User` section, specify `{{item.output}}` as `Ground truth`, and specify `{{sample.output_text}}` as `Output`. This will take the original reference output from your evaluation `.jsonl` file (the sample file provided) and compare it against the output that is generated by the model you chose in the previous step.
 
     :::image type="content" source="../media/how-to/evaluations/eval-semantic-similarity-2.png" alt-text="Screenshot of the semantic similarity UX config." lightbox="../media/how-to/evaluations/eval-semantic-similarity-2.png":::
 
@@ -206,7 +204,7 @@ You will select the model of your choice. If you do not have a model, you can cr
 
 ## Types of Testing Criteria
 
-Azure OpenAI Evaluation offers various evaluation testing criteria on top of Semantic Similarity we saw in the example above. This section provides information about each testing criteria at much more detail.
+Azure OpenAI Evaluation offers various evaluation testing criteria on top of Semantic Similarity we saw in the provided example. This section provides information about each testing criteria at much more detail.
 
 ### Factuality