TW Review

Author: Karuna-Mendix

content/en/docs/marketplace/genai/mendix-cloud-genai/Mx GenAI Connector.md

@@ -32,7 +32,7 @@ The Mendix Cloud GenAI Connector is commonly used for text generation, embedding
     * Simulate characters for games
     * Image to text
 
-#### Embeddings generation
+#### Embeddings Generation
 
 Convert strings into vector embeddings for various purposes based on the relatedness of texts.
 
@@ -51,8 +51,6 @@ You can combine embeddings with text generation capabilities and leverage specif
 The Mendix Cloud GenAI Connector module generates embeddings internally when interacting with a knowledge base. Pure embedding operations are only required if additional processes, such as using the generated vectors instead of text, are needed. For example, a similar search algorithm could use vector distances to calculate relatedness.
 {{% /alert %}}
 
-
-
 #### Knowledge Base
 
 The module enables tailoring generated responses to specific contexts by grounding them in data inside of a collection belonging to a Mendix Cloud GenAI knowledge base resource. This allows for the secure use of private company data or other non-public information when interacting with GenAI models within the Mendix app. It provides a low-code solution to store discrete data (commonly called chunks) in the knowledge base and retrieves relevant information for end-user actions or application processes.
@@ -65,9 +63,8 @@ Knowledge bases are often used for:
 If you are looking for a step-by-step guide on how to get your application data into a Mendix Cloud Knowledge Base, refer [Grounding Your Large Language Model in Data – Mendix Cloud GenAI](/appstore/modules/genai/how-to/howto-groundllm/). Note that the Mendix Portal also provides options for importing data into your knowledge base, such as file uploads. For more information, see [Navigate through the Mendix Cloud GenAI Portal](/appstore/modules/genai/mx-cloud-genai/Navigate-MxGenAI/). This documentation focuses solely on adding data from an application using the connector. 
 
 ##### Architecture
-A Knowledge Base resource can comprise several collections. Each collection is specifically designed to hold numerous documents, serving as a logical grouping for related information based on their shared domain, purpose, or thematic focus. While collections provide a mechanism for data separation—with each corresponding to a [GenAICommons.DeployedKnowledgebase](/appstore/modules/genai-commons/#deployed-knowledge-base) — it is not best practice to create a large number of collections within a single Knowledge Base resource. A more performant and practical approach for achieving fine-grained data separation is through the strategic use of metadata. You can learn about this in [Retrieve and Generate](/appstore/modules/genai/MxGenAI/#retrieve-and-generate).
-
 
+A Knowledge Base resource can comprise several collections. Each collection is specifically designed to hold numerous documents, serving as a logical grouping for related information based on its shared domain, purpose, or thematic focus. While collections provide a mechanism for data separation, with each corresponding to a [DeployedKnowledgebase](/appstore/modules/genai/genai-for-mx/commons/#deployed-knowledge-base), it is not best practice to create a large number of collections within a single Knowledge Base resource. A more performant and practical approach for achieving fine-grained data separation is through the strategic use of metadata. To learn more, see [Retrieve and Generate](/appstore/modules/genai/mx-cloud-genai/MxGenAI-connector/#retrieve-and-generate).
 
 ### Features
 

content/en/docs/marketplace/genai/mendix-cloud-genai/mendix-cloud-grp.md

@@ -24,13 +24,13 @@ Mendix Cloud GenAI Resource Packs is a premium Mendix product that requires an a
 
 Mendix Cloud Model Resource Packs provide customers with a monthly quota of input and output tokens for Anthropic's Claude and Cohere's Embed models. This allows customers to implement typical Generative AI use cases using text generation, embeddings, and knowledge bases.
 
-### Supported models
+### Supported Models
 
 The Mendix Cloud GenAI Resource Packs provide access to the following models:
 
 * Anthropic Claude 3.5 Sonnet v1
-* Anthropic Claude 3.7 Sonnet (Cross-Region Inference Profile)
-* Cohere Embed v3 (English & multilingual options)
+* Anthropic Claude 3.7 Sonnet (Cross-region inference profile)
+* Cohere Embed v3 (English and multilingual options)
 
 The models are available through the Mendix Cloud, leveraging AWS's highly secure Amazon Bedrock multi-tenant architecture. This architecture employs advanced logical isolation techniques to effectively segregate customer data, requests, and responses, ensuring a level of data protection that aligns with global security compliance requirements. Customer prompts, requests, and responses are neither stored nor used for model training. Your data remains your data.
 
@@ -76,10 +76,11 @@ The Mendix Portal allows easy access to manage the resources, through the GenAI
 
 ## Mendix Cloud GenAI Connector
 
-The [Mendix Cloud GenAI connector](/appstore/modules/genai/mx-cloud-genai/MxGenAI-connector/) lets you utilize Mendix Cloud GenAI resource packs directly within your Mendix application. It allows you to integrate generative AI by dragging and dropping common operations from its toolbox. Please note that versions older than the ones listed below do no longer work:  
+The [Mendix Cloud GenAI connector](/appstore/modules/genai/mx-cloud-genai/MxGenAI-connector/) lets you utilize Mendix Cloud GenAI resource packs directly within your Mendix application. It allows you to integrate generative AI by dragging and dropping common operations from its toolbox. Note that any versions older than the ones listed below are no longer functional:  
+
 * GenAI for Mendix bundle v2.4.1 (Mendix 9) (contains Mendix Cloud GenAI connector) or
-* Mendix Cloud GenAI connector v3.1.1 (no DeployedKnowledgeBase support) or
-* Mendix Cloud GenAI connector v4.4.0 (DeployedKnowledgeBase support).
+* Mendix Cloud GenAI connector v3.1.1 (no `DeployedKnowledgeBase` support) or
+* Mendix Cloud GenAI connector v4.4.0 (`DeployedKnowledgeBase` support).
 
 ## Regional Availability
 

content/en/docs/marketplace/genai/mendix-cloud-genai/navigate_mxgenai.md

@@ -85,7 +85,7 @@ When you select the **Add Files Like .TXT or .PDF** option, you can upload docum
 
 {{% alert color="info" %}} Only TXT and PDF files are supported. {{% /alert %}} 
 
-Before the upload, you can decide for yourself, whether to upload data to a new, the default or a different existing collection inside of the resource. A Knowledge Base resource can comprise several collections. Each collection is specifically designed to hold numerous documents, serving as a logical grouping for related information based on their shared domain, purpose, or thematic focus. While collections provide a mechanism for data separation—with each corresponding to a [GenAICommons.DeployedKnowledgebase](/appstore/modules/genai-commons/#deployed-knowledge-base) — it is not best practice to create a large number of collections within a single Knowledge Base resource. A more performant and practical approach for achieving fine-grained data separation is through the strategic use of metadata.   
+Before uploading, you can choose to upload the data to a new collection, the default collection, or another existing collection within the resource. A Knowledge Base resource can comprise several collections. Each collection is specifically designed to hold numerous documents, serving as a logical grouping for related information based on its shared domain, purpose, or thematic focus. While collections provide a mechanism for data separation, with each corresponding to a [DeployedKnowledgebase](/appstore/modules/genai/genai-for-mx/commons/#deployed-knowledge-base), it is not best practice to create a large number of collections within a single Knowledge Base resource. A more performant and practical approach for achieving fine-grained data separation is through the strategic use of metadata.   
 
 ##### Metadata {#metadata}