Artificial Intelligence (AI) is no longer just about chatbots and static responses — it’s now an active part of modern software development. One standout feature driving this change is OpenAI’s Function Calling, which allows AI models to perform real actions in your applications, from retrieving live project data to updating your systems in real time.
Here, we’ll dive into how function calling transforms AI from a passive tool into a dynamic business partner. We’ll also explore what this means for both business leaders and developers. By the end, you’ll see why this technology is a game-changer for building smarter, more powerful software solutions for your organization.
Business Insights: Unlocking New Potential with LLMs
While OpenAI’s ChatGPT is a remarkable conversational agent, it has inherent limitations.
Understanding the Limitations of Out-of-the-Box ChatGPT
The foundational model essentially operates on static data, relying solely on the information available in its training dataset. This means that, although the AI can generate unique, human-like responses, it cannot access real-time information or private data sources critical for many business applications.
For instance, if a business wanted to use ChatGPT to assist customers with their current orders, the LLM would be unable to do so without a special connection to the relevant data. This static nature significantly constrains the functionality of LLMs out-of-the-box. Furthermore, when you prompt LLMs to answer questions they lack critical information about, they often mislead users by fabricating responses. (This is a phenomenon known as “hallucination.”)
The Power of Function Calling: Connecting with Real-Time Data
Given the limitations of static data discussed above, function calling fundamentally changes the game. By implementing Function Calling, developers enable the LLM to access specific data that businesses choose to reveal, effectively bridging the gap between static knowledge and dynamic interaction.
With function calling, the AI can retrieve data from your backend systems and perform actions on that data, such as manipulating it or storing it on demand. For example, a project management tool could allow the AI to fetch the latest project timelines, update statuses in real-time, or even initiate workflows based on user commands.
This feature is designed with safety in mind, giving developers full control over the actions the LLM can perform. The interface lets developers directly specify what data the LLM can access and which functions it is permitted to execute. This means sensitive information remains protected, and the AI’s reach into your system is limited. In other words, business leaders need not worry about this feature resulting in LLMs encroaching into unwanted areas of the application.
Delivering Greater Value
Function calling enables application developers to harness the full potential of AI while aligning technology with business goals. By allowing real-time data interactions and controlled functionality, developers can create tailored solutions that address specific challenges within their organizations.
This results in enhanced efficiency, allowing businesses to automate routine tasks and focus on strategic initiatives. Additionally, the ability to provide personalized user experiences translates into improved customer engagement and satisfaction. Ultimately, Function Calling establishes a pathway for businesses to innovate and remain competitive in an increasingly digital landscape.
Software Maker Insights: Leveraging Function Calling in Your Application
Leveraging function calling within OpenAI’s API is a straightforward process.
How it Works
The chat/completions API endpoint enables developers to inform the LLM about a set of functions that are available in the application and specify the parameters required for each function. While the API cannot automatically call these functions, the responses from the LLM can be used to trigger the function calls as needed.
Below is a look at how to configure the function calling mechanism. In this code snippet, you can see an example of the tools array, which is passed to the LLM during an interaction. This array informs the LLM of the list of available functions it can access, along with their descriptions and necessary parameters. Note: currently Functions are the only type of tool that is supported.
tools: [
{
type: "function",
function: {
name: "get_project_status",
description: "Fetches the status of the specified project.",
parameters: {
type: "object",
properties: {
project_id: {
type: "string",
description: "The ID of the project"
}
},
required: ["project_id"]
}
}
}
]Using this setup, when the user interacts with the LLM, the LLM will know that it can call the custom function — in this case, get_project_status()— when a project status is needed. The API uses a specially formatted response to indicate when a function call is needed as a result of the user’s prompt. The key elements of this type of response are highlighted in the snippet below.
{
"choices": [
{
"message": {
"role": "assistant",
"tool_calls": [
{
"id": "call_123abc",
"type": "function",
"function": {
"name": "get_project_status",
"arguments": "{n"project_id": "X123"n}"
}
}
]
},
"finish_reason": "tool_calls"
}
]
}
Notice in this example, the finish_reason is indicated as tool_calls, and the tool_calls array specifies that the get_project_status function should be called.
After making the requested function call, the final step is to return the result to the LLM. This is done by making another request to the chat/completions API and including a new entry in the messages array of the request body. For example, the messages array for the full sequence might look like this:
messages: [
// User asks a question...
{role: "user", content: "What's the status of project X?"},
// LLM requests a function call...
{role: "assistant", tool_calls: [{type: "function", function: {name: "get_project_status", arguments: '{"project_id": "X"}'}, id: "1234"}]},
// Application calls function and encodes result in json...
{role: "tool", name: "get_project_status", content: '{"percent_complete": "95", "status": "on_track"}', tool_call_id: "1234"},
// LLM provides context-rich response...
{role: "assistant", content: "Great news! Project X is on track and already 95% complete! The team is really crushing it!" }
]Using Helper Libraries
As a developer building GenAI-integrated applications, it is important to understand how Function Calling works. However, it’s not necessary to code the entire Function Calling process yourself. OpenAI provides several open-source libraries that make interacting with their LLM very simple. In addition to streamlining the function calling experience, these libraries also simplify authentication, data streaming, file uploads, and other more advanced operations. For more information, check out the repositories below:
Comparing LLMs
OpenAI was the first major LLM to incorporate function calling natively into their API, which is why I am centering my post this way. That said, OpenAI is not the only LLM that supports Function Calling. Check out the list below for the docs to the Function Calling feature for all the big LLM players.
- Anthropic announced in early 2024 that Claude now supports function calling through a feature they call, Tool Use.
- Meta’s open-source LLM, LLaMa, has also adopted support for Function Calling.
- Details on Google Gemini’s support for function calling can be found here.
Driving Innovation with AI-Integrated Apps
The introduction of function calling by OpenAI represents a significant leap forward in how artificial intelligence can be integrated into custom software applications. By overcoming the limitations of static language models and providing a controlled interface for accessing and manipulating real-time data, function calling opens up a world of possibilities for both developers and business leaders. It allows businesses to leverage the power of AI not just for generating content or answering questions but for executing meaningful, data-driven actions that enhance user experiences and streamline workflows.
As we continue to explore the potential of AI in software development, function calling stands out as a pivotal feature that not only deepens the utility of large language models but also makes them safer and more tailored to specific business needs. Whether you’re looking to enhance customer engagement, optimize internal processes, or create entirely new kinds of interactions, the combination of LLMs and function calling offers a path forward that blends innovation with practical value.
By understanding and leveraging these capabilities, businesses can stay ahead of the curve in an increasingly competitive landscape, turning cutting-edge AI into solutions that are as dynamic and responsive as their users demand.