> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rovax.io/llms.txt
> Use this file to discover all available pages before exploring further.

# LLM Node (AI Transformation)

> Process text with a language model directly in the workflow — without an agent persona.

## How It Works

The **AI Transformation** node makes a direct call to a language model with a prompt you define. There is no agent persona or conversation history.

The execution flow follows five steps:

1. Resolves `{{ }}` templates in the input field and prompt with real execution data
2. Assembles the model call with the prompt and configured parameters
3. Waits for the response — 30-second timeout
4. Parses the result: JSON when `output_format` is `json`, string when it is `text`
5. Exposes the result via `{{ $json.result }}` for downstream nodes

## Configuration Options

* **Input Field** — `{{ }}` expression pointing to the text to process
* **Prompt** — instruction for the model; accepts `{{ }}` templates with dynamic data
* **Model** — `gpt-4o-mini`, `gpt-4o`, `claude-3-haiku`, or `claude-3-sonnet`
* **Output format** — `text` for a free string or `json` for a structured object
* **JSON Schema** — optional; guides the model on the expected fields when `output_format` is `json`
* **Temperature / Max Tokens** — variation control (0.0 to 1.0) and token limit (100 to 4000)

## Common Use Cases

* Extract entities from unstructured forms or emails
* Classify intent or sentiment to route service tickets
* Summarize long tickets before notifying the team
* Normalize date, phone, or address formats

## Example

Prompt:

```text theme={null}
Extract the name, email, and reason from the text below.
Return only valid JSON.

Text: {{ $trigger.body.mensagem }}
```

Output with `output_format: json`:

```json theme={null}
{
  "result": {
    "nome": "Carlos Menezes",
    "email": "carlos@empresa.com",
    "motivo": "dúvida sobre faturamento"
  }
}
```

## Tip

<Note>
  Use a temperature between 0.0 and 0.2 for extraction and classification — deterministic responses reduce failures in downstream nodes that consume the generated JSON.
</Note>