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content/en/docs/genai/concepts/_index.md

@@ -133,7 +133,7 @@ An example of how this can be done with OpenAI is described in [RAG Example Impl
 
 Another way to provide the LLM with additional information and capabilities is to use function calling, also known as tool use. With function calling you can make specific microflows available to the LLM. While evaluating the prompt, the LLM will, optionally, ask to execute a particular microflow. The Mendix application will execute this microflow and return additional information for the LLM to add to the prompt being processed.
 
-This microflow runs in the context of the user, allowing you to make sure that it only shows data that is relevant for the current user. You can also use it to execute actions on behalf of the user, or interact with page that the user is looking at.
+This microflow runs in the context of the user, allowing you to make sure that it only shows data that is relevant for the current user. You can also use it to execute actions on behalf of the user, or interact with the page that the user is looking at.
 
 See [Function Calling](/agents/function-calling/) for more information on ReAct. You can see ReAct implemented in the [GenAI Showcase App](https://marketplace.mendix.com/link/component/220475) where the `GetInformationForTicketID` microflow allows an LLM to answer a question like "What is the status of ticket 42?".
 

content/en/docs/genai/concepts/model-context-protocol.md

@@ -28,7 +28,7 @@ The MCP host is typically the application that facilitates interaction with LLMs
 
 ### MCP Client
 
-The MCP client usually runs inside of the MCP Host and is responsible of connecting to one MCP server outside of the MCP host application. The client follows the MCP principles and should technically be able to connect to any MCP server. A host can maintain multiple clients at the same time and thus connecting to multiple MCP servers or maintaining a client-server connection for each end-user.
+The MCP client usually runs inside of the MCP Host and is responsible for connecting to one MCP server outside of the MCP host application. The client follows the MCP principles and should technically be able to connect to any MCP server. A host can maintain multiple clients at the same time and thus connecting to multiple MCP servers or maintaining a client-server connection for each end-user.
 
 ### MCP Server
 
@@ -38,7 +38,7 @@ Servers can expose the following three main types of resources:
 
 #### Tools
 
-The most common type of resource is [tool](https://modelcontextprotocol.io/specification/2025-06-18/server/tools), which exposes executable functionality such as microflows to clients. Those can not only contain simple retrieve logic to get information from a database, but also perform actions on behalf of the user, for example, submitting a report or changing values. Typically, a model in the MCP host chooses when to call a tool based on the context of the chat.
+The most common type of resource is a [tool](https://modelcontextprotocol.io/specification/2025-06-18/server/tools), which exposes executable functionality such as microflows to clients. Those can not only contain simple retrieve logic to get information from a database, but also perform actions on behalf of the user, for example, submitting a report or changing values. Typically, a model in the MCP host chooses when to call a tool based on the context of the chat.
 
 #### Prompts
 
@@ -52,7 +52,7 @@ In general, generic [resources](https://modelcontextprotocol.io/specification/20
 
 An example for setting up an MCP server is available in the [GenAI Showcase App](https://marketplace.mendix.com/link/component/220475). The app guides you through available tools and prompts that will be exposed when the server is started. Additionally, it shows an example for setting up authentication using username and password. You can use a Mendix application or third-party tools such as Claude Desktop as an MCP host to connect to the server.
 
-Furthermore, an MCP Client example teaches you how to establish a connection to a server, discover tools and prompts, and finally uses them in a chat interface. You can use this example to inspect MCP servers, similar as the [MCP Inspector](https://github.com/modelcontextprotocol/inspector) can do, by testing prompts and tools without any LLM integration.
+Furthermore, an MCP Client example teaches you how to establish a connection to a server, discover tools and prompts, and finally use them in a chat interface. You can use this example to inspect MCP servers, similar as the [MCP Inspector](https://github.com/modelcontextprotocol/inspector) can do, by testing prompts and tools without any LLM integration.
 
 ## Read More
 

content/en/docs/genai/concepts/prompt-engineering.md

@@ -90,7 +90,7 @@ Different prompt techniques can be used to guide AI models in performing their s
 | Prompt technique | Description | Use Case and Prompt Example |
 |-------------------|---------------------------------------------------|----------------------------------|
 | Interview Pattern Approach| Using an interview-style approach allows the model to ask follow-up questions to the end-user to provide a better-fitting response. | Use case: Movie Recommendation Engine.<br>Prompt instruction: *You will act as a movie recommender expert. You will ask the user a series of detailed questions, one at a time, to understand their preferences in movies*. |
-| Instruction Prompt | The instruction gives the AI model directions on how to perform a task. It can be a guidance on the type of output, such as summarization or translation, style, format, and more. | Use case: Mendix ML Kit Python Script Generator.<br>Prompt instruction: *You will act as an expert Python developer specializing in the Mendix ML Kit. The output/response should be given in a python script with annotations for the Mendix ML Kit*. |
+| Instruction Prompt | The instruction gives the AI model directions on how to perform a task. It can be guidance on the type of output, such as summarization or translation, style, format, and more. | Use case: Mendix ML Kit Python Script Generator.<br>Prompt instruction: *You will act as an expert Python developer specializing in the Mendix ML Kit. The output/response should be given in a python script with annotations for the Mendix ML Kit*. |
 | Few-shot Prompt | It helps the model to learn a task or pattern dynamically by providing examples. It can also be part of the system prompt. | Use case: English-Spanish Translator.<br>Prompt instruction: *You are a kind assistant who helps translate English texts to Spanish. For example, “Good Evening” to Spanish: “Buenas Noches*” |
 | Chain-of-Thought | It simplifies complex tasks by turning them into discrete steps that happen in a certain order. | Use case: Medical Diagnosis for interns.<br>Prompt instruction: *You are a diagnosis assistant designed to help trainee doctors ask a series of questions for patients’ initial evaluation. Your goal is to identify the patient’s symptoms, health history, and other relevant variables to reach an accurate evaluation that, depending on the result, will be forwarded to nurses or doctors. Start by asking the patient about their primary symptoms and the reason for their visit. Then, ...* |
 | Tree-of-Thought | Similar to a decision tree, it includes several lines of thought to allow the model to evaluate and find its path to the correct outcome. | Use case: Support Assistant Bot.<br>Prompt instruction: *You are a helpful assistant supporting the IT department with employees’ requests, such as support tickets, licenses, or hardware inquiries. Follow the instructions below according to the type of request. If the user asks about ... If the request is vague or incomplete, ... If the request is about licenses or hardware, first ... then ... If the user wants to know about their support tickets, ...* |

content/en/docs/genai/concepts/rag-example-implementation.md

@@ -10,7 +10,7 @@ aliases:
 
 ## Introduction {#introduction}
 
-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. 
+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 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](/agents/get-started/#rag).{{% /alert %}}
 
@@ -126,4 +126,4 @@ If you want to build your own RAG setup, feel free to learn from the GenAI Showc
 
 ## Read More {#read-more}
 
-* [Embeddings-based Search – Open AI Cookbook](https://cookbook.openai.com/examples/question_answering_using_embeddings)
+* [Embeddings-based Search – OpenAI Cookbook](https://cookbook.openai.com/examples/question_answering_using_embeddings)

content/en/docs/genai/mendix-cloud-genai/navigate-mxgenai.md

@@ -22,7 +22,7 @@ After clicking a specific resource, you land on its details page, which provides
 
 {{% alert color="info" %}} The **Consumption** tab is available for Model resources only.{{% /alert %}} 
 
-{{% alert color="warning" %}}A resource reaches its monthly GenAI Unit allocation is blocked. A blocked resource stays blocked until a company admin increases its GenAI Unit capacity or the current bundle month ends. For details, see [Consumption Limit Notifications](#consumption-limit-notifications).{{% /alert %}}
+{{% alert color="warning" %}}A resource that reaches its monthly GenAI Unit allocation is blocked. A blocked resource stays blocked until a company admin increases its GenAI Unit capacity or the current bundle month ends. For details, see [Consumption Limit Notifications](#consumption-limit-notifications).{{% /alert %}}
 
 The **Consumption** tab provides an overview of GenAI Unit and Large Language Model (LLM) token consumption for the resource. Use this overview to see current usage, view daily consumption insights, and compare the current period with previous periods. 
 
@@ -32,7 +32,7 @@ Note that periods represent bundle months. A bundle month is the period during w
 
 **Current Consumption** shows the total GenAI Units consumed against your monthly allocation, displayed as a percentage and a number (for example, 1,521 / 1.6k). **Plan** shows the resource pack model type (for example, Anthropic Claude Opus) and the total GenAI Unit allocation for the bundle month. The bundle refill date is shown at the top of the page.
 
-Below the summary, the page shows **Consumption Over Selected Time Range**. You can switch between weekly (**W**), bundle month (**BM**), month to date (**MTD**), and six months (**6M**) views. Use the **Model** dropdown to filter the charts by a specific model, and the **Key** dropdown to filter them by a specific resource key. The charts show the following metrics:
+Below the summary, the page shows **Consumption Over Selected Time Range**. You can switch between weekly (**W**), bundle month (**BM**), month to date (**MTD**), and six months (**6M**) views. Use the **Model** drop-down list to filter the charts by a specific model, and the **Key** dropdown to filter them by a specific resource key. The charts show the following metrics:
 
 * **Monthly Bundle GenAI Units Consumption** – GenAI Units consumed per day, with a dashed line indicating the monthly allocation limit.
 * **Monthly Bundle Input Consumption** – Raw LLM input tokens consumed per day.
@@ -48,7 +48,7 @@ Mendix notifies you when a resource approaches or reaches its monthly GenAI Unit
 
 A blocked resource stays blocked until one of the following happens:
 
-* A company admin increases the GenAI Unit capacity of the resource so that it covers to a value above the current consumption. For more information, see [Adjusting the GenAI Unit Capacity of a Resource](#adjusting-the-genai-unit-capacity-of-a-resource).
+* A company admin increases the GenAI Unit capacity of the resource so that it covers a value above the current consumption. For more information, see [Adjusting the GenAI Unit Capacity of a Resource](#adjusting-the-genai-unit-capacity-of-a-resource).
 * The current bundle month ends and the allocation resets.
 
 To avoid blocking, increase the allocation as soon as you receive the 80% or 100% notification.

content/en/docs/genai/v1/how-to/byo_connector.md

@@ -59,7 +59,7 @@ If your provider's API differs significantly from OpenAI's, it is best to start
 Additionally, refer to the [GenAI Commons](/agents/agents-kit-1/reference-guide/commons/) to explore available out-of-the-box components that can help accelerate development. Pay close attention to:
 
 * The domain model (data structure) to see how existing entities can be reused. 
-* The **Connector Building** folders, contain useful microflows and helper activities for working with the provided entities.
+* The **Connector Building** folders contain useful microflows and helper activities for working with the provided entities.
 
 If you want to explore the [GenAICommons](https://marketplace.mendix.com/link/component/227933) module, check out the [public repository](https://github.com/mendix/genai-showcase-app).
 
@@ -155,4 +155,4 @@ To set this up:
 1. Find **Echo Configurations** in the **Management** section of the home page. This will lead you to the page where the configuration can be set up for the Echo Connector.
 2. Click **New**, fill in the required fields, and click **Save**. For this example, the input can be left empty as no real credentials are needed. When you click **Save**, two `EchoDeployedModel` objects are created for the new configuration. Since the Echo Connector simply returns the request content as the response, these serve as test models for the Chat UI examples. In a custom connector, this step could involve importing available models based on the configuration or allowing the admin to create models manually.
 3. After the configuration and the models have been created, go back to the home page and open one of the showcases in the **Conversational UI** section.
-4. In the **Model** dropdown, select one of the models created by the Echo Connector and start chatting.
+4. In the **Model** drop-down list, select one of the models created by the Echo Connector and start chatting.

content/en/docs/genai/v1/how-to/creating-agents/shared-setup.md

@@ -206,7 +206,7 @@ You have now successfully created your first function microflow to link to the a
 #### Get Ticket by Identifier
 
 1. Create a new microflow named `Ticket_GetTicketByID`.
-2. In the new microflow, Add a *String* input parameter called `Identifier`.
+2. In the new microflow, add a *String* input parameter called `Identifier`.
 
 3. Add a `Retrieve` action to retrieve the ticket of the given identifier:
 

content/en/docs/genai/v1/how-to/ground_your_llm_in_data.md

@@ -45,7 +45,7 @@ Before implementing this capability into your app, make sure you meet the follow
 
 Since this document focuses on the [Mendix Cloud GenAI Resource Packs](/agents/mx-cloud-genai/resource-packs/), ensure that you have the [Mendix Cloud GenAI Connector](https://marketplace.mendix.com/link/component/239449) installed.
 
-Follow the instructions in the [Navigate through the Mendix Cloud GenAI Portal](/agents/mx-cloud-genai/Navigate-MxGenAI/) guide to collect the resources keys and configure the connector within your application. The keys bridge the gap between your app and the resources, enabling you to access models and add to or retrieve data from a Mendix Cloud GenAI knowledge base.
+Follow the instructions in the [Navigate through the Mendix Cloud GenAI Portal](/agents/mx-cloud-genai/Navigate-MxGenAI/) guide to collect the resource keys and configure the connector within your application. The keys bridge the gap between your app and the resources, enabling you to access models and add to or retrieve data from a Mendix Cloud GenAI knowledge base.
 
  While this documentation focuses on adding data to your knowledge base from a Mendix application, you can also fill the knowledge base directly within the portal, for example, by uploading files.
 

content/en/docs/genai/v1/how-to/integrate_function_calling.md

@@ -26,7 +26,7 @@ Through this document, you will:
 
 Before integrating function calling into your app, make sure you meet the following requirements:
 
-* An existing app: To simplify your first use case, start building from a preconfigured set up [Blank GenAI Starter App](https://marketplace.mendix.com/link/component/227934). For more information, see [Build a Chatbot from Scratch Using the Blank GenAI App](/agents/agents-kit-1/how-to/blank-app/). 
+* An existing app: To simplify your first use case, start building from the preconfigured [Blank GenAI Starter App](https://marketplace.mendix.com/link/component/227934). For more information, see [Build a Chatbot from Scratch Using the Blank GenAI App](/agents/agents-kit-1/how-to/blank-app/). 
 
 * Be on Mendix Studio Pro 10.12.4 or higher.