Automating repetitive writing tasks boosts productivity, but connecting a user interface to a Large Language Model traditionally requires complex API integrations. Non-technical teams often struggle to build custom internal tools because writing the logic for prompts, timeouts, and databases is technically prohibitive. By utilizing Momen’s visual development environment, you can architect a robust AI text completion tool that securely processes inputs and generates formatted text—without writing a single line of code.
What Is an AI Text Completion Tool And When To Use It
An AI text completion tool takes brief user inputs—like bullet points or partial sentences—and outputs fully expanded, context-aware responses. It standardizes content creation and speeds up drafting workflows.
Typical Use Cases:
- Expanding meeting notes into client emails
- Generating product descriptions from feature tags
When NOT to use it:
Avoid for highly technical, factual research where AI hallucinations pose compliance risks.
To understand the underlying model configurations, refer to the AI Agent Overview documentation. For a related use case, see how structured AI feedback works in the [How to Build An AI Copywriting Style Reviewer]
How To Build This In Momen
How to Implement AI Predictive Text Input
Project Access Link
- Method 1 (Multi-Option List): View project
- Method 2 (Streaming Completion): View project
Introduction
Goal: To enhance user experience by providing intelligent, real-time text predictions based on initial input.
Which Method to Choose?
Method 1: Multi-Option List
Overview:
Provides multiple distinct AI-generated options for the user to choose from, saving all suggestions to the database.
Applicable Scenario:
Search bar suggestions, AI brainstorming tools, or email subject generators.
Method 2: Real-Time Streaming
Overview: Offers a single, real-time, character-by-character prediction that acts as an inline suggestion to seamlessly complete the current sentence.
- Applicable Scenario: Fast chat applications, text editors, or quick form filling.
Core Logic:
Method 1: Triggered by input -> Async Actionflow -> AI Agent (Structured Array) -> Database Table -> UI List (Subscription mode).
Method 2: Triggered by input -> AI Agent (Streaming Output) -> Page Variable -> UI Text component.
Method 1: Multi-Option List
This method generates a list of suggestions stored in the database, allowing users to choose from several distinct completions.
Data Storage
To handle the asynchronous nature of AI generation, we store suggestions in a table so the frontend can "subscribe" to them.
Data Model:
Create a table named suggestion_record.
AI Agent Configuration
The agent must return structured data to allow the backend to process multiple options.
AI Studio:
Create an agent Agent_Input_Prediction.Inputs:
Add user_input (Text).Prompt Template:
Role: You are a predictive text assistant.
Goals: "Based on {Input.user_input}, return 3 different completion options. Max 10 words each."
Output Configuration:
Set to Structured. Define an ARRAY(STRING) field named suggestions.
Actionflow Construction
- Name:
AI Predictive Text Input
- Execution Mode:
Set to Async.
- Nodes:
Input Node:
Define user_input (Text).AI Node:
Select Agent_Input_Prediction. Bind its input to the Actionflow parameter.Loop (loop save suggestions):
Datasource: Select the suggestions array from the Agent_Input_Prediction (Agent_Input_Prediction.data.suggestions).
- Insert Data (save suggestion): Inside the loop, insert a record into suggestion_record.
- user_input: Bind to the Actionflow's user_input.
- suggested_text: Bind to item (the current suggestion in the loop).
- conversation_id: Bind to the id from the Agent_Input_Prediction.
UI Construction & Interaction
1.Input Trigger:
On the Text input On change event, add a condition STRING_LEN(Inputs.Text input) >= 3. If met, run the Actionflow.
2.The List:
Place a Conditional Container below the input, setting its display condition to STRING_LEN(Inputs.Text input) >= 3. Inside it, place a List component.
- Data Source: Remote -> suggestion_record.
- Request Type: Subscription (Required to see data as it's inserted). Limit the data to 3.
- Filter: user_input equals Inputs.Text input.
- Sort: Sort by ID in Descending order to ensure the latest predictions appear at the top.
- Bind the text component inside the list to display the suggestion.
3.Fill Action:
On clicking the List item, use "Set input component value" to fill the input with item.suggested_text. (Toggle OFF "Trigger object value change behavior")
Verification
- Trigger: Type a short phrase (at least 3 characters) into the input field.
- Observe: After a brief processing moment, a list containing 3 different AI-generated completion options will appear below the input.
- Interact: Click on any of the suggested options.
- Result: The input field is instantly populated with your selected text, and the suggestion list disappears.
Method 2: Real-Time Streaming
This method provides an immediate, character-by-character prediction that streams directly into the UI.
Logic & State Configuration
Instead of a database, we use a Page Variable for temporary storage and immediate display.
AI Agent Configuration
- AI Studio: Create an agent (e.g., Agent_Text_Prediction).
- Prompt Template: Role: You are a predictive text assistant.
- Goals: "Based on the text provided: '{Input.user_input}', predict and complete the content."
- Output Configuration: Set type to Plain text.
- Streaming: Toggle Streaming output to ON.
Page Variable Setup
- In the Pages -> Data panel, create a Page variable named user_input (Type: Text).
UI Construction & Interaction
1.The Canvas:
- Place a Text input for the user.
- Place a Text component bound to Page variable.user_input.
2.Streaming Interaction:
- Select the Text input. Under Interaction -> On change:
- Condition: STRING_LEN(Inputs.Text input) >= 3.
- Action: AI -> Start conversation.
- Config: Select the streaming agent and set Append streaming output to -> Page variable.user_input.
3.Accept Suggestion:
- Select the Text component (the suggestion). Under On click:
- Action 1: "Set input component value" -> Target: Text input -> Value: Page variable.user_input. (Toggle OFF "Trigger object value change behavior")
- Action 2: "Set page variable" -> Reset user_input to Empty Text.
Verification
- Trigger: Type a short phrase (at least 3 characters) into the input field.
- Observe: Below the input box, the predicted text will dynamically generate, appearing character-by-character in real-time. 3.Interact: Click on the generated prediction text.
- Result: The content automatically fills into the main input box, and the temporary prediction below is immediately cleared.
Building a generative AI app does not mean starting from a blank canvas. We encourage you to explore the pre-configured project template directly.
Duplicate the project into your workspace to see exactly how the UI binds to the Actionflow and AI Agent. From there, customization is straightforward. By simply altering the AI prompt instructions, you can shift the app's output from drafting professional corporate emails to generating casual, creative storytelling.
Next, consider exploring advanced configurations. Features like vector storage for retrieval-augmented generation (RAG) can ground your text completion tool in company-specific data for highly tailored responses.
Conclusion
Creating a robust AI text completion workflow requires establishing a solid data structure, configuring a specialized Momen AI agent, and connecting them through a precise backend logic flow.
Momen abstracts the heavy lifting of complex API integrations and database management. This allows builders to focus entirely on visual logic and prompt design while maintaining full architectural control over the application.
Clone the project template today, modify the AI instructions to fit your specific writing needs, and launch your automated text expansion tool.
If you are willing to try it yourself, you could also clone this project's multi-option list or streaming completion.
