# Ask ChatGPT
**Action ID:** `openai_ask_chat_gpt`
## Description
Use AI to ask a question and get a response from OpenAI's ChatGPT model.
## Provider
OpenAI
## Connection
| Name | Description | Required | Category |
| ----------------- | ------------------------------------------ | -------- | -------- |
| OpenAI Connection | The OpenAI connection to use for the chat. | True | OpenAI |
## Input Parameters
| Name | Type | Required | Default | Description |
| ------------------ | -------- | -------- | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| model | dropdown | ✓ | gpt-4o-mini | The model to use for the chat. Options: gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-4o, gpt-4o-mini, o3, o3-mini, o1, o1-mini |
| prompt | string | ✓ | - | The question to ask the model. |
| temperature | number | - | 0.9 | Controls randomness: Lowering results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive. (Range: 0.0-1.0) |
| max\_tokens | integer | - | 2048 | The maximum number of tokens to generate. Requests can use up to 2,048 or 4,096 tokens shared between prompt and completion depending on the model. |
| top\_p | number | - | 1.0 | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top\_p probability mass. |
| frequency\_penalty | number | - | 0.0 | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. |
| presence\_penalty | number | - | 0.6 | Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. |
| system\_message | string | - | You are a helpful assistant. | Instructions for the AI assistant on how to behave and respond. |
View Technical Schema
```json
{
"description": "AskChatGPTInput",
"properties": {
"model": {
"type": "string",
"title": "Model",
"description": "The model to use for the chat.",
"default": "gpt-4o-mini"
},
"prompt": {
"type": "string",
"title": "Question",
"description": "The question to ask the model."
},
"temperature": {
"type": "number",
"title": "Temperature",
"description": "Controls randomness: Lowering results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive.",
"default": 0.9,
"minimum": 0.0,
"maximum": 1.0
},
"max_tokens": {
"type": "integer",
"title": "Maximum Tokens",
"description": "The maximum number of tokens to generate. Requests can use up to 2,048 or 4,096 tokens shared between prompt and completion depending on the model.",
"default": 2048
},
"top_p": {
"type": "number",
"title": "Top P",
"description": "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.",
"default": 1.0
},
"frequency_penalty": {
"type": "number",
"title": "Frequency penalty",
"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.",
"default": 0.0
},
"presence_penalty": {
"type": "number",
"title": "Presence penalty",
"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the mode's likelihood to talk about new topics.",
"default": 0.6
},
"system_message": {
"type": "string",
"title": "System Message",
"description": "Instructions for the AI assistant on how to behave and respond.",
"default": "You are a helpful assistant."
}
},
"required": ["model", "prompt"]
}
```
## Output Parameters
| Name | Type | Description |
| ------- | ------ | ---------------------------------- |
| content | string | The response content from ChatGPT. |
View Technical Schema
```json
{
"description": "AskChatGPTOutput",
"properties": {
"content": {
"type": "string",
"title": "Content",
"description": "The response content from ChatGPT."
}
},
"required": ["content"]
}
```
## How It Works
This node sends your question to OpenAI's ChatGPT API along with your configuration parameters. The model processes your prompt using the system message as behavioral context, then generates a response based on the temperature and other generation settings you've configured. The response is returned as text that can be used by subsequent nodes in your workflow.
## Usage Examples
### Example 1: Simple Question
**Input:**
```
model: "gpt-4o-mini"
prompt: "What are the benefits of cloud computing?"
temperature: 0.3
max_tokens: 500
```
**Output:**
```
content: "Cloud computing offers several key benefits: 1) Cost Savings - Pay only for resources you use without capital expenses for hardware. 2) Scalability - Easily scale up or down based on demand. 3) Accessibility - Access data and applications from anywhere with internet..."
```
### Example 2: Creative Writing
**Input:**
```
model: "gpt-4o"
prompt: "Write a short story about an AI learning to paint"
temperature: 0.9
max_tokens: 2048
system_message: "You are a creative writer who specializes in science fiction short stories."
presence_penalty: 0.8
```
**Output:**
```
content: "The First Brushstroke\n\nADAM-7 had analyzed thousands of paintings, from da Vinci to Pollock, yet the blank canvas before it remained intimidating. Its neural networks hummed with uncertainty—a sensation its creators never anticipated..."
```
### Example 3: Technical Analysis
**Input:**
```
model: "o1"
prompt: "Analyze the trade-offs between microservices and monolithic architecture"
temperature: 0.2
max_tokens: 1500
system_message: "You are a software architecture expert. Provide detailed technical analysis."
frequency_penalty: 0.3
```
**Output:**
```
content: "Microservices vs. Monolithic Architecture Analysis:\n\nMonolithic Architecture:\nPros: Simpler deployment, easier debugging, better performance for small apps...\n\nMicroservices Architecture:\nPros: Independent scalability, technology flexibility, fault isolation..."
```
## Common Use Cases
* **Customer Support Automation**: Generate intelligent, context-aware responses to customer inquiries
* **Content Generation**: Create blog posts, product descriptions, and marketing copy at scale
* **Code Assistance**: Get help with code generation, debugging, and technical explanations
* **Data Analysis**: Analyze and summarize complex data, reports, and research findings
* **Translation**: Translate content between languages while maintaining tone and context
* **Educational Content**: Generate explanations, tutorials, and learning materials
* **Creative Writing**: Produce stories, scripts, poetry, and other creative content
## Error Handling
| Error Type | Cause | Solution |
| ------------------------ | ------------------------------------------------ | ------------------------------------------------------------------------- |
| Authentication Error | Invalid or missing OpenAI API key | Verify your OpenAI connection is properly configured with a valid API key |
| Rate Limit Exceeded | Too many requests in a short time period | Implement delays between requests or upgrade your OpenAI plan |
| Token Limit Exceeded | Prompt + response exceeds model's maximum tokens | Reduce prompt length or decrease max\_tokens parameter |
| Invalid Model | Model name doesn't exist or no access | Verify the model name is correct and available in your OpenAI account |
| Content Policy Violation | Prompt or response violates OpenAI policies | Revise prompt to comply with OpenAI's usage policies |
| Timeout Error | Request took too long to process | Try a faster model or reduce max\_tokens |
| Insufficient Quota | OpenAI account has insufficient credits | Add credits to your OpenAI account or check billing settings |
## Notes
* **Temperature Control**: Use lower temperatures (0.0-0.3) for factual, consistent responses. Use higher temperatures (0.7-1.0) for creative, varied content.
* **Model Selection**: Choose based on your needs. GPT-4o offers best performance for complex tasks, while GPT-4o-mini is cost-effective for simpler queries. O1 and O3 models excel at reasoning tasks.
* **Token Management**: Be mindful of max\_tokens setting. Each model has different context windows. Monitor usage to optimize costs.
* **System Messages**: Craft clear, specific system messages to guide the AI's behavior, tone, and response format effectively.
* **Penalty Parameters**: Use frequency\_penalty to reduce repetition and presence\_penalty to encourage topic diversity in responses.
* **Top P vs Temperature**: Use either top\_p or temperature for randomness control, not both. Top\_p (nucleus sampling) is generally more stable.
* **Cost Optimization**: Use GPT-4o-mini for simple tasks to reduce costs. Reserve GPT-4o and O-series models for complex reasoning or analysis.
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.agenticflow.ai/reference/nodes/openai-ask-chat-gpt.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.