TW Review: Astral, OpenAI and Amazon Bedrock

Author: Karuna-Mendix

content/en/docs/marketplace/genai/reference-guide/external-platforms/mistral.md

@@ -13,7 +13,7 @@ The [Mistral Connector](https://marketplace.mendix.com/link/component/248276) al
 
 ### Features {#features}
 
-The Mistral Connector is commonly used for text generation based on the [Chat Completions API](https://docs.mistral.ai/api/endpoint/chat) and embeddings generation with the [Embeddings API](https://docs.mistral.ai/api/endpoint/embeddings). Typical use cases for generative AI are described in more detail [here](/appstore/modules/genai/get-started/#llm-use-cases).
+The Mistral Connector is commonly used for text generation based on the [Chat Completions API](https://docs.mistral.ai/api/endpoint/chat) and embeddings generation with the [Embeddings API](https://docs.mistral.ai/api/endpoint/embeddings). Typical use cases for generative AI are described in the [Typical LLM Use Cases](/appstore/modules/genai/get-started/#llm-use-cases).
 
 For more information about the models, see [Mistral models](https://docs.mistral.ai/getting-started/models).
 
@@ -25,7 +25,6 @@ Mistral does not currently offer image generation models out of the box. It is p
 
 The Mistral connector supports Knowledge bases from providers such as pgVector, Mendix Cloud, Amazon Bedrock, and Azure AI Search to be added to a conversation.
 
-
 ### Prerequisites
 
 To use this connector, you need to sign up for a Mistral account and create an API key. For more information, see the [Quickstart guide](https://docs.mistral.ai/getting-started/quickstart).
@@ -130,7 +129,7 @@ Mistral does not directly connect to the knowledge resources. The model returns
 
 This functionality is part of the implementation executed by the GenAI Commons Chat Completions operations mentioned earlier. As a developer, you need to make the system aware of your indexes and their purpose by registering them with the request. This is done using the GenAI Commons operation [Tools: Add Knowledge Base](/appstore/modules/genai/genai-for-mx/commons/#add-knowledge-base-to-request), which must be called once per knowledge resource before passing the request to the Chat Completions operation.
 
-Note that the retrieval process is independent of the model provider and can be used with any model that supports function calling, thanks to the generalized `GenAICommons.DeployedKnowledgeBase` input parameter.
+Note that the retrieval process is independent of the model provider and can be used with any model that supports function calling, as it relies on the generalized `GenAICommons.DeployedKnowledgeBase` input parameter.
 
 #### Vision {#chatcompletions-vision}
 

content/en/docs/marketplace/genai/reference-guide/external-platforms/openai.md

@@ -25,11 +25,11 @@ Mendix provides dual-platform support for both [OpenAI](https://platform.openai.
 
 With the current version, Mendix supports the Chat Completions API for [text generation](https://platform.openai.com/docs/guides/text-generation), the Image Generations API for [images](https://platform.openai.com/docs/guides/images), the Embeddings API for [vector embeddings](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings), and indexes via [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/) for knowledge base retrieval.
 
-Typical use cases for generative AI are described in more detail [here](/appstore/modules/genai/get-started/#llm-use-cases).
+Typical use cases for generative AI are described in the [Typical LLM Use Cases](/appstore/modules/genai/get-started/#llm-use-cases).
 
 #### Knowledge Base
 
-By integrating Azure AI Search, the OpenAI Connector allows for knowledge base retrieval from Azure datsources. For Retrieval Augmented Generation (RAG) scenarios, chat completions with (Azure) OpenAI can also be combined with knowledge bases by other provider such as Mendix Cloud.
+By integrating Azure AI Search, the OpenAI Connector enables knowledge base retrieval from Azure data sources. For Retrieval Augmented Generation (RAG) scenarios, chat completions with (Azure) OpenAI can also be combined with knowledge bases by other provider such as Mendix Cloud.
 
 ### Prerequisites {#prerequisites}
 
@@ -183,7 +183,7 @@ OpenAI does not directly connect to the Azure AI Search resource. The model retu
 
 This functionality is part of the implementation executed by the GenAI Commons Chat Completions operations mentioned earlier. As a developer, you need to make the system aware of your indexes and their purpose by registering them with the request. This is done using the GenAI Commons operation [Tools: Add Knowledge Base](/appstore/modules/genai/genai-for-mx/commons/#add-knowledge-base-to-request), which must be called once per index before passing the request to the Chat Completions operation.
 
-Note that the retrieval process is independent of the model provider and can be used with any model that supports function calling, thanks to the generalized `GenAICommons.DeyploedKnowledgeBase`entity.
+Note that the retrieval process is independent of the model provider and can be used with any model that supports function calling, as it relies on the generalized `GenAICommons.DeyploedKnowledgeBase`entity.
 
 #### Vision {#chatcompletions-vision}
 

content/en/docs/marketplace/platform-supported-content/modules/aws/amazon-bedrock.md

@@ -21,7 +21,7 @@ Amazon Bedrock is a fully managed service that makes foundation models (FMs) fro
 * Generating images based on text prompts and displaying them in the Mendix app.
 * Generating embedding vectors for text inputs.
 
-Typical use cases for generative AI are described in more detail [here](/appstore/modules/genai/get-started/#llm-use-cases).
+Typical use cases for generative AI are described in the [Typical LLM Use Cases](/appstore/modules/genai/get-started/#llm-use-cases).
 
 ### Prerequisites {#prerequisites}