What Your Endpoint Returns Matters - LLM Function Calling

Today, I want to talk about something critical—something many users overlook at first: the response your endpoint returns when your chatbot triggers a function call.

The real magic with Large Language Models (LLMs) happen when they interact with external systems. Function calling is the bridge that connects LLMs to your existing infrastructure, allowing them to retrieve information and take actions based on user requests.

Sounds technical? Stick with me; it's simpler than you think!

First, What's Function Calling?

In a nutshell, OpenAI's Large Language Models (LLMs), like GPT, can do more than chat. They can also call external functions defined by you—the user. These functions help your chatbot interact with external systems, fetch real-time information, store data, or trigger workflows.

We've classified these interactions into two main categories:

Data Collection/Action-Oriented: Triggering emails, storing leads, or managing infrastructure.
Information Retrieval: Fetching real-time data, personalized user information, or structured data from unstructured text.

Let's dive deeper into how function calling works from an OpenAI perspective.

How Does OpenAI Trigger a Function?

Here's the simple flow:

A user sends a request to your chatbot.
The LLM identifies if an external function needs to be called (based on what you've configured).
If yes, the chatbot triggers your predefined function automatically.

What Happens Next in Your Predictable Dialogs Setup?

You've already set up your function calls with:

HTTP Method (GET or POST)
Endpoint URL
Bearer Token (for security)

Here's the easy breakdown:

If you've chosen GET: The arguments identified by the chatbot are sent as query parameters.
Example: Your chatbot helps users check the weather. The LLM triggers a GET request to your endpoint:
```
https://api.yourweather.com/current?city=London
```
If you've selected POST: Arguments are sent in the body of the request.
Example: Collecting a lead's info into your CRM:
```
POST https://api.yourcrm.com/leads
{
  "name": "Jane Doe",
  "email": "jane@example.com"
}
```

Crucial Point: What Your Endpoint Returns Matters!

Here's the real deal:

The response your endpoint sends back isn't just a technicality. It's the information your chatbot uses to continue the conversation. The chatbot takes this returned data, interprets it through OpenAI's models, and generates the next user reply based on it.

Example Scenario (Action-Oriented): Your chatbot triggers a "send welcome email" function.

Endpoint returns:

{ "status": "success", "message": "Welcome email sent successfully." }

Chatbot replies:
"Great news! We've successfully sent your welcome email. Check your inbox!"

Example Scenario (Information Retrieval): Your chatbot checks stock prices.

Endpoint returns:

{ "ticker": "AAPL", "price": 185.32, "currency": "USD" }

Chatbot replies:
"The current price of Apple stock (AAPL) is $185.32 USD."

See? The endpoint's response directly shapes your chatbot's next message.

Best Practices for Endpoint Responses

Ensure your endpoint's responses are:

Clear & structured: JSON format is ideal.
Relevant & concise: Include only the information needed.
Consistent: A stable response structure helps the LLM provide accurate replies.

Conclusion

To get the most out of your chatbot's function calls, remember: What your endpoint returns really matters! A clear and structured response helps your chatbot serve your users seamlessly and intelligently.

Got questions or experiences to share? We'd love to hear from you!