Text Generation

The Text Generation Node enables users to automatically generate text or structured data using advanced AI models. This node can be used for a wide range of purposes — such as creating summaries, generating responses, writing articles, or producing structured JSON data — without requiring any coding knowledge.

It forms a key component in workflows where natural language generation or intelligent content creation is required.

Generate text or structured data using AI models.

This node uses AI models to process user instructions and produce text or JSON output. It is fully compatible with no-code workflows, meaning users can simply configure key parameters through an intuitive interface without writing scripts or API calls.

The platform provides free, in-house AI models for general and creative tasks:

• aicon-v4-nano-160824 – for simple or lightweight tasks. Does not support file attachments.
• aicon-v4-large-160824 – for complex text generation and file-based processing (supports file attachments).
• aicon-v4-search-160824 – for online-based search tasks that search, analyze, and summarize in one step.

For users requiring more precision, reasoning depth, or specialized capabilities, there is an option to connect premium external models from providers like OpenAI, Anthropic, or Google. These models can be enabled from the billing page and are billed at cost. Available external models include:

The node accepts a flat list of key-value inputs. Each parameter controls specific aspects of how the AI generates content.

{{nodeId.input.<key>}}

{{textGeneration.input.prompt}}
{{textGeneration.input.generationType}}

After execution, the node returns several outputs that represent both the AI's result and processing information.

{{textGeneration.output.content}}
{{textGeneration.output.processingId}}
{{textGeneration.output.processingTime}}
{{textGeneration.output.processingCount}}
{{textGeneration.output.conversationId}}

{{textGeneration.output.title}}
{{textGeneration.output.summary}}
{{textGeneration.output.point1}}
{{textGeneration.output.processingId}}
{{textGeneration.output.processingTime}}
{{textGeneration.output.processingCount}}
{{textGeneration.output.conversationId}}

Output Type: text | json

The node's outputType input parameter determines how the output is structured:

• "text" → Returns content field containing the generated text, along with processing metadata.
• "json" → Returns flattened keys from jsonContent directly in the output (not nested under content), along with processing metadata.

For Text Output:

{{nodeId.output.content}}
{{nodeId.output.processingId}}
{{nodeId.output.processingTime}}
{{nodeId.output.processingCount}}
{{nodeId.output.conversationId}}

For JSON Output:

{{nodeId.output.<key1>}}
{{nodeId.output.<key2>}}
{{nodeId.output.processingId}}
{{nodeId.output.processingTime}}
{{nodeId.output.processingCount}}
{{nodeId.output.conversationId}}

Where <key1>, <key2>, etc. are the keys defined in your jsonContent schema.

Example 1: Generate Text

Goal: Write a paragraph about AI ethics.

Input Configuration:

{  "generationType": "aicon-v4-nano-160824",  "prompt": "Write a paragraph explaining the importance of AI ethics.",  "outputType": "text"}

Output Example:

{  "output": {    "content": "AI ethics are a crucial framework for navigating the complex landscape of artificial intelligence development and deployment. The six key principles outlined in this image – Transparency, Accountability, Mitigating Bias, Fairness, Security, and Privacy – collectively highlight why ethical considerations are paramount. Without transparency, it's difficult to understand how AI systems make decisions, hindering trust and accountability. Unchecked bias can lead to discriminatory and harmful outcomes, emphasizing the need for robust mitigation strategies. Fairness, while challenging to define, is essential to ensure equitable treatment for all. Robust security measures are vital to protect AI systems from malicious attacks and preserve public trust, while privacy safeguards individual autonomy regarding personal data. Adhering to these principles ensures that AI systems are developed responsibly, beneficially, and without causing undue harm, fostering public confidence and promoting a positive societal impact from this transformative technology.",    "processingTime": 5178,    "processingId": "google-520649282babb51d",    "processingCount": 439,    "conversationId": "71e9512ea89b9e9f0d1d174369678b85"  }}

Accessing Values Inside the Workflow:

{{textGeneration.output.content}}        → Generated text
{{textGeneration.output.processingTime}} → 5178
{{textGeneration.output.processingId}}   → "google-520649282babb51d"

Example 2: Generate Structured JSON

Goal: Extract key points from an article.

Input Configuration:

{  "generationType": "aicon-v4-large-160824",  "prompt": "Provide a paragraph about Healthcare 4.0.",  "outputType": "json",  "jsonContent": {    "title": "string",    "content": "string",    "summary": "string",    "point1": "string",    "point2": "string",    "point3": "string"  }}

Output Example:

{  "output": {    "title": "Healthcare 4.0: The Future of Medicine",    "content": "Healthcare 4.0 represents the transformative integration of advanced digital technologies into the healthcare ecosystem, marking a significant evolution from traditional medical practices. This paradigm leverages technologies such as Artificial Intelligence (AI), Machine Learning (ML), the Internet of Medical Things (IoMT), big data analytics, blockchain, and robotics to create a more personalized, predictive, preventive, and participatory healthcare experience. It aims to enhance operational efficiency, improve patient outcomes, reduce costs, and make healthcare more accessible and equitable globally. From precision medicine tailored to individual genetic profiles to AI-powered diagnostics and robotic surgery, Healthcare 4.0 is redefining how medical services are delivered, managed, and consumed, fostering an era of smart, connected, and data-driven healthcare solutions.",    "summary": "Healthcare 4.0 integrates advanced digital technologies like AI, IoMT, and big data into healthcare for a more personalized, predictive, and efficient system, improving patient outcomes and accessibility.",    "point1": "Utilizes AI, Machine Learning, and IoMT for enhanced diagnostics and personalized treatment plans.",    "point2": "Focuses on predictive and preventive care through data analytics, shifting from reactive to proactive healthcare.",    "point3": "Improves operational efficiency, reduces costs, and increases healthcare accessibility through digital transformation.",    "processingTime": 2414,    "processingId": "google-b3437be5929daeaa",    "processingCount": 371,    "conversationId": "b0a5d25e4b46980ed8c8d3c04e7f6bf3"  }}

Accessing Values Inside the Workflow:

{{textGeneration.output.title}}        → "Healthcare 4.0: The Future of Medicine"
{{textGeneration.output.content}}      → Full content paragraph
{{textGeneration.output.summary}}      → Summary text
{{textGeneration.output.point1}}       → First key point
{{textGeneration.output.point2}}      → Second key point
{{textGeneration.output.point3}}      → Third key point
{{textGeneration.output.processingTime}} → 2414
{{textGeneration.output.processingCount}} → 371

Note: When outputType is "json", the keys from jsonContent are flattened and appear directly in the output alongside the processing metadata. There is no content field for JSON output type.

Example 3: File-Based Processing with aicon-v4-large

Goal: Analyze a document and extract key information.

Input Configuration:

{  "generationType": "aicon-v4-large-160824",  "prompt": "Summarize the main points from the attached document.",  "attachments": "file-id-123,file-id-456",  "outputType": "text"}

Note: File attachments are only supported by aicon-v4-large-160824. The aicon-v4-nano-160824 model does not support file attachments.

Example 4: Online Search with aicon-v4-search

Goal: Search the internet and get a summarized analysis.

Input Configuration:

{  "generationType": "aicon-v4-search-160824",  "prompt": "What are the latest developments in quantum computing? Search online and provide a summary.",  "outputType": "text"}

Note: The aicon-v4-search-160824 model performs online-based search, analysis, and summarization in one step.

Do / Don't

Use Case: Generate and send AI-generated report automatically.

1. Trigger Node – REST API Trigger (receives input data such as topic or user query.)
2. AI Node – Text Generation Node (creates the report based on the input.)
3. Process Node – Return State Node (displays the generated output.)

Additional Use Cases

1. Customer Support Chatbot

A customer sends a message via WhatsApp asking about product features:

Workflow:

1. WhatsApp Trigger – Receives customer message.
2. Text Generation Node – Generates a helpful response using aicon-v4-nano-160824 with conversationId for context.
3. Send Message Node – Sends the AI-generated response back to the customer.

2. Document Analysis and Summarization

A user uploads a document that needs to be analyzed:

Workflow:

1. File Upload Trigger – Receives the document file.
2. Text Generation Node – Uses aicon-v4-large-160824 with attachments parameter to analyze the document.
3. Database Node – Stores the summary in a database.
4. Return State Node – Returns the analysis results.

3. Online Research Assistant

A user requests information about a current topic:

Workflow:

1. REST API Trigger – Receives research query.
2. Text Generation Node – Uses aicon-v4-search-160824 to search online and provide a summary.
3. Email Node – Sends the research results via email.

4. Structured Data Extraction

Extract structured information from unstructured text:

Workflow:

1. Email Trigger – Receives an email with unstructured data.
2. Text Generation Node – Uses outputType: "json" with a flat schema to extract structured data.
3. Database Node – Stores the extracted structured data.

5. Multi-Turn Conversation Handler

Maintain context across multiple interactions:

Workflow:

1. REST API Trigger – Receives user message and conversation ID.
2. Text Generation Node – Uses conversationId to maintain context and generate contextual responses.
3. Return State Node – Returns the response with the same conversationId for next interaction.