fixing broken links

Author: Karuna-Mendix

content/en/docs/appstore/use-content/platform-supported-content/modules/genai/_index.md

@@ -25,7 +25,7 @@ Mendix supports a variety of generative AI tasks by integrating with tools such
 
 To start using the GenAI capabilities of Mendix, complete the following tasks:
 
-1. Familiarize yourself with [concepts](/appstore/modules/genai/using-gen-ai/) such as prompt engineering, Retrieval Augmented Generation (RAG) and function calling (ReAct).
+1. Familiarize yourself with [concepts](/appstore/modules/genai/get-started/) such as prompt engineering, Retrieval Augmented Generation (RAG) and function calling (ReAct).
 2. Select the right architecture to support your use case. For a full list of possibilities, see [Architecture and Components](#architecture).
 3. Obtain the required credentials for your selected architecture.
 

content/en/docs/appstore/use-content/platform-supported-content/modules/genai/concepts/prompt-engineering.md

@@ -73,7 +73,7 @@ After telling the model what to do, you can include additional context. This can
 
 * information about the end-user of your application – for example their language, role, department, specific database records
 * context information – for example, all data related to an object the end-user is looking at
-* knowledge coming from [Retrieval Augmented Generation (RAG)](/appstore/modules/genai/using-gen-ai/#rag)
+* knowledge coming from [Retrieval Augmented Generation (RAG)](/appstore/modules/genai/get-started/#rag)
 
 Tip: you can provide information in a JSON or XML structure to ensure the information is presented in a consistent way. From Mendix apps, you can use [Export Mappings](/refguide/export-mappings/) to create JSON structures and [Export XML Documents](/howto/integration/export-xml-documents/) to create XML structures.
 

content/en/docs/appstore/use-content/platform-supported-content/modules/genai/concepts/rag-example-implementation.md

@@ -11,7 +11,7 @@ description: "Describes the retrieval augmented generation (RAG) pattern and the
 
 Retrieval augmented generation (RAG) is a framework for an AI-based search using a private or external knowledge base that combines embeddings-based knowledge retrieval with a text generation model. The starting point is a collection of data to be considered as the private knowledge base. The final goal is that an end user of the app can ask questions about the data and the assistant's responses are only be based on this knowledge base. 
 
-{{% alert color="info" %}}This document describes how to set up RAG with PgVector. If you want to use the Bedrock Retrieval Augmented Generation capabilities, see [Bedrock Retrieval Augmented Generation](/appstore/modules/genai/using-gen-ai/#rag).{{% /alert %}}
+{{% alert color="info" %}}This document describes how to set up RAG with PgVector. If you want to use the Bedrock Retrieval Augmented Generation capabilities, see [Bedrock Retrieval Augmented Generation](/appstore/modules/genai/get-started/#rag).{{% /alert %}}
 
 ### Terminology
 

content/en/docs/appstore/use-content/platform-supported-content/modules/genai/conversational-ui.md

@@ -19,7 +19,7 @@ Typical use cases for Conversational UI include the following:
 * Create a chat interface for users to chat with Large Language Models (LLM). 
 * Allow users to switch between different implementations by switching providers. 
 * Include advanced capabilities to control the model's behavior, for example, by setting the temperature parameter.
-* Easily extend the chat interface with advanced concepts, such as RAG or the ReAct pattern. For more information, see [Using Generative AI](/appstore/modules/genai/using-gen-ai/).
+* Easily extend the chat interface with advanced concepts, such as RAG or the ReAct pattern. For more information, see [Get Started with Generative AI](/appstore/modules/genai/get-started/).
 
 ### Features {#features}