5 Powerful GPT Function Calling Use Cases You Shouldn’t Miss
By Max Brodeur-Urbas
What Are GPT Functions?
OpenAI’s Function Calling feature might be the most revolutionary — yet underappreciated — capability ever released by the company. GPT functions allow you to transform unstructured data into structured data, unlocking a level of precision and automation previously unattainable.
While this may not sound groundbreaking at first, consider that over 90% of global data entry and processing jobs revolve around this task. GPT function calling is a quiet revolution that can replace or dramatically streamline these efforts in AI workflows.
Ever felt frustrated trying to get GPT-3.5 or GPT-4 to return just the answer you need — no fluff, disclaimers, or polite acknowledgments? GPT Function Calling is the direct solution.
How GPT Functions Work
OpenAI provides limited documentation on how function calling works, so developers often rely on community examples. Here’s a simplified breakdown:
- Function Definition: You define a function in JSON, including its name, description, and input parameters.
- Response Generation: GPT doesn’t call the function, but it generates a structured response that a system can pass to a real API.
There are two levels of inference when using function calling:
- Function Selection: GPT selects the relevant function from a list of candidates passed into the model via its API. Overloading the prompt with too many options can hurt response quality and efficiency.
- Parameter Filling: Think of giving GPT a form — it uses context, reasoning, and your parameter descriptions to decide how to fill the form accurately.
Top 5 GPT Function Calling Use Cases
1. Data Extraction
Function calling is excellent for extracting structured data—such as names, emails, phone numbers—from unstructured text. This is especially useful in situations like contest submissions, where entries must be parsed for essential details automatically.
Example: Extract an email address from a submission.
{
"name": "update_email",
"description": "Updates email based on the content of their submission.",
"parameters": {
"type": "object",
"properties": {
"email": {
"type": "string",
"description": "The email provided in the submission"
}
},
"required": ["email"]
}
}
2. Scoring
Use GPT to assign scores to text data using flexible criteria. This is invaluable for evaluating sentiment, grading essays, or assessing the quality of sales leads.
Example: Score sales leads based on firmographics and job title.
{
"name": "update_sales_lead_value_score",
"description": "Updates the score of a sales lead and provides a justification",
"parameters": {
"type": "object",
"properties": {
"sales_lead_value_score": {
"type": "number",
"description": "An integer value from 0 to 100..."
},
"score_justification": {
"type": "string",
"description": "Justification for the score"
}
},
"required": ["sales_lead_value_score", "score_justification"]
}
}
3. Categorization
With GPT functions, you can define custom categories and have GPT assign input data accordingly. This is incredibly useful for labeling articles, support tickets, or product types.
Example: Classify news articles into topics like US Politics, Pandemic, or Pop Culture.
{
"name": "categorize",
"description": "Categorize the input data into user defined buckets.",
"parameters": {
"type": "object",
"properties": {
"category": {
"type": "string",
"enum": ["US Politics", "Pandemic", "Economy", "Pop culture", "Other"],
"description": "..."
},
"justification": {
"type": "string",
"description": "Short justification"
}
},
"required": ["category", "justification"]
}
}
4. Option Selection
Present GPT with a list of choices and have it select the best option based on contextual relevance. This use case is ideal for bots that need to rank content or provide recommendations.
Example: Select the top AI-related post from Hacker News headlines.
{
"name": "find_best_post",
"description": "Determine the best post that most closely reflects the query.",
"parameters": {
"type": "object",
"properties": {
"best_post_title": {
"type": "string",
"description": "The title of the best post"
}
},
"required": ["best_post_title"]
}
}
5. Filtering
This is a streamlined version of categorization, where GPT determines if an input meets a specific condition. Great for automating validation processes or moderation workflows.
Example: Validate contest entries to ensure they comply with all requirements.
{
"name": "apply_condition",
"description": "Used to decide whether the input meets the user provided condition.",
"parameters": {
"type": "object",
"properties": {
"decision": {
"type": "string",
"enum": ["True", "False"],
"description": "True if meets requirements, False otherwise."
}
},
"required": ["decision"]
}
}
Build Your Own AI Workflows with AgentHub
If you’re interested in putting GPT Function Calling to work in your own no-code environment, check out AgentHub. It’s a drag-and-drop platform for building AI-powered workflows — many of which use GPT functions as the cohesive force behind them.
Popular templates include:
- Sales Lead Scoring
- News Article Categorization
- Twitter Bot Option Selection
Start building today at agenthub.dev, and join their thriving Discord community for tips, support, and community resources.
Follow @AgentHub_AI or the author for more updates on AI-driven development.
