Text Generator

Simple single-turn apps for generating text from a prompt and user inputs

Text Generators are simple single-turn apps: you write a prompt, provide inputs, and the model generates a response.

It’s a good fit for tasks that don’t require multi-turn conversation, tool calls, or a multi-step workflow. Just a clear input, one model call, and a ready-to-use output.

Configure

Write the Prompt

The prompt tells the model what to do, how to respond, and what constraints to follow.

Since a Text Generator runs in a single turn with no conversation history, the prompt is the model’s only source of context—include everything it needs to produce the right output in one pass.

Here are some tips for writing effective prompts:

• Define the task clearly: State what the model should produce (e.g., a translation, a summary, a SQL statement).

• Specify the output format: Describe the structure, length, or style you expect.

• Set constraints: Tell the model what to avoid or what rules to follow.

Because a Text Generator always requires user input to run, a paragraph-type query variable is automatically inserted into the prompt when you create a new app. You can rename query or change its type.

Variables are placeholders—each one becomes an input field that users fill in before running the app, and their values are substituted into the prompt at runtime. For example:

You are a professional editor. Summarize the following text into 3 concise bullet points. Use neutral tone and avoid adding information not present in the original text.

{{query}}

While drafting the prompt, type / > New Variable to quickly insert a named placeholder. You can configure its details in the Variables section later.

Choose the variable type that matches the input you expect:

Short Text

Accepts up to 256 characters. Use it for names, email addresses, titles, or any brief text input that fits on a single line.

Paragraph

Allows long-form text without length restrictions. It gives users a multi-line text area for detailed descriptions.

Select

Displays a dropdown menu with predefined options.

Number

Restricts input to numerical values only—ideal for quantities, ratings, IDs, or any data requiring mathematical processing.

Checkbox

Provides a simple yes/no option. When a user checks the box, the output is true; otherwise, it’s false. Use it for confirmations or any case that requires a binary choice.

API-based Variable

Fetches variable values from an external API at runtime instead of collecting them from users.

Use it when your prompt needs dynamic data from an external source, such as live weather conditions or database records. See API Extension for details.

Label Name is what end users see for each input field.

Create Dynamic Prompts with Variables

To adapt your app to different users or contexts without rewriting the prompt each time, add more variables.

Each variable collects a specific piece of information upfront and injects it into the prompt at runtime.

For example, an SQL generator might use database_type to adapt the output dialect while query captures the user’s natural language request:

You are an SQL generator. Translate the following natural language query into a {{database_type}} SQL statement: {{query}}

Generate or Improve the Prompt with AI

If you’re unsure where to start or want to refine the existing prompt, click Generate to let an LLM help you draft it.

Describe what you want from scratch, or reference current_prompt and specify what to improve. For more targeted results, add an example in Ideal Output.

Each generation is saved as a version, so you can experiment and roll back freely.

Ground Responses in Your Own Data

To ground the model’s responses in your own data rather than general knowledge, add a knowledge base and select an existing variable as the Query Variable.

When a user runs the app and fills in that field, its value is used as the search query to retrieve relevant content from the knowledge base. The retrieved content is then injected into the prompt as context, so the model can generate a more informed response.

Example: Use Content Type to Retrieve Style Guides in a Content Writing App

For example, suppose your knowledge base contains style guides for different content types—blog posts, social media captions, product descriptions, and so on.

In a content writing app, set content_type as the Query Variable. When a user selects a content type, the app retrieves the matching style guide and generates copy that follows the corresponding writing standards.

Your prompt might look like this:

You are a brand content writer. Write a {{content_type}} based on the following brief: {{brief}}

Follow the style and tone guidelines provided in the context.

Configure App-Level Retrieval Settings

To fine-tune how retrieval results are processed, click Retrieval Setting.

There are two layers of retrieval settings—the knowledge base level and the app level.

Think of them as two consecutive filters: the knowledge base settings determine the initial pool of results, and the app settings further rerank the results or narrow down the pool.

• Rerank Settings

  • Weighted Score

    The relative weight between semantic similarity and keyword matching during reranking. Higher semantic weight favors meaning relevance, while higher keyword weight favors exact matches.

    Weighted Score is available only when all added knowledge bases are indexed with High Quality mode.

  • Rerank Model

    The rerank model to re-score and reorder all the results based on their relevance to the query.

    If any multimodal knowledge bases are added, select a multimodal rerank model (marked with a Vision tag) as well. Otherwise, retrieved images will be excluded from reranking and the final output.

• Top K

  The maximum number of top results to return after reranking.

  When a rerank model is selected, this value will be automatically adjusted based on the model’s maximum input capacity (how much text the model can process at once).

• Score Threshold

  The minimum similarity score for returned results. Results scoring below this threshold are excluded. Use higher thresholds for stricter relevance or lower thresholds to include broader matches.

Search Within Specific Documents

By default, retrieval searches across the entire knowledge base. To restrict retrieval to specific documents, enable manual or automatic metadata filtering.

This improves retrieval precision, especially when your knowledge base is large or contains content for different contexts.

For creating and managing document metadata, see Metadata.

Process Multimodal Inputs

To allow users to upload images, audio, or documents when using the app, select a model that supports the corresponding modalities and enable specific file types—Vision, Audio, and Document.

You can quickly identify a model’s supported modalities by its tags.

Click Settings under Vision to configure how files are accepted and processed. Upload settings apply across all enabled file types.

• Resolution: Controls the detail level for image processing only.

  • High: Better accuracy for complex images but uses more tokens

  • Low: Faster processing with fewer tokens for simple images

• Upload Method: Choose whether users can upload from their device, paste a URL, or both.

• Upload Limit: The maximum number of files a user can upload per run.

Text Generators also support optional features like generating multiple outputs at once, text to speech, and content moderation. See App Toolkit for details.

Debug & Preview

In the preview panel on the right, you can test your app in real time. Select a model that best fits your task, fill in the input fields, and click Run to see the output.

After selecting a model, you can adjust its parameters to control how it generates responses. Available parameters and presets vary by model.

To compare outputs across different models, click Debug as Multiple Models to run up to 4 models simultaneously.

Publish

When you’re happy with the results, click Publish to make your app available. See Publish for the full list of publishing options.

When running the web app, users can save individual outputs for future reference.