Inference • Last Updated 6/3/2024

How function calling makes your AI applications smarter

Learn about function calling and how to use it to generate structured output from large language models (LLMs).


By Fiona McDonnell

Imagine if your AI applications could interact with real-world systems effortlessly, turning complex requests into smooth, actionable outcomes.

Function calling does just that, making your AI smarter and more efficient. By enabling AI to call specific functions, you can streamline processes, reduce errors, and enhance user experiences. This breakthrough bridges the gap between AI logic and practical implementation, making it an essential tool for developers aiming to innovate and optimize.

Keep reading to learn how function calling can elevate your AI applications and transform the way you approach automation and problem-solving.

What is function calling?

Function calling is a fundamental concept in programming and software development that enhances the efficiency and organization of code. In artificial intelligence, function calling allows AI systems to interpret user commands and execute specific tasks dynamically, resulting in more context-aware and intuitive interactions.

At its core, function calling involves defining reusable blocks of code, known as functions, which can be invoked whenever needed to perform specific tasks. Function calling enables developers to write more efficient, scalable code across various domains, from web and game development to data analysis and embedded systems.

This modular approach promotes code reusability and maintainability and simplifies complex operations by breaking them down into manageable components.

Function calling in AI

Function calling is a powerful tool that allows AI applications to use real-world data to make decisions. In AI, function calling is particularly useful because it allows applications to process and respond to input data in real time. This capability enhances the system's flexibility and efficiency. It’s crucial for developing responsive, interactive AI applications, bridging the gap between human intentions and machine responses.

By directly calling functions, developers can bypass some of the limitations of standard request-response models used in many APIs.

How does function calling work?

Functions are described using function declarations. Here’s how that works:

  1. Function declarations are sent to an LLM.
  2. The model returns a structured output that includes the name of the functions and their arguments.
  3. The LLM will try to answer the initial user quey with one of the returned functions.

It’s important to note that the LLM doesn’t actually call the function, it’s the schema object output that calls the function returned by the model.

This process differs from merely sending and receiving data as JSON objects in a predefined format. With function calling, AI applications can instead execute complex operations on the fly, adapting to data they receive without the need for manual intervention.

Function calling vs. JSON mode

While function calling involves a JSON schema output, there are differences between the function calling and JSON mode. Both are powerful, but they serve slightly different purposes and are suited for different types of tasks.

Real-time data handling

One key difference between function calling and JSON mode is how data is handled and processed. JSON mode typically involves sending and receiving static data that doesn't change until the next request is made. As we’ve explored above, function calling allows for dynamic interaction with data, enabling AI applications to adjust their operations in real time based on the latest data inputs.

Contextual awareness

Function calling enhances the contextual awareness of AI applications thanks to its interactions with real-time data. For example, an AI application that uses function calling can analyze incoming data, determine the information's relevance, and decide on the appropriate function to call in response. This process is less feasible in JSON mode, which struggles with complex conditional logic.

Benefits of function calling in AI applications

Using function calling in AI applications has several main benefits:

Increased efficiency

Function calling significantly increases the efficiency of AI applications by calling functions directly. This method enables faster processing times and reduces latency, which is crucial for applications requiring immediate data analysis and action.

Enhanced flexibility

Developers can easily update and modify functions without overhauling the entire application, allowing for quicker adaptations to new requirements or changes in the operational environment.


Function calling facilitates scalability, as new functions can be added and called as needed without extensive modifications to the existing infrastructure. This capability makes it easier for businesses to scale their operations up or down with minimal disruption.

With its real-time data processing capabilities, contextual adaptability, and enhanced efficiency, function calling can unlock greater flexibility and real-time responsiveness in your AI applications.

Getting started with function calling in AI

When getting started with function calling, there are some best practices to consider. Developers should focus on keeping functions modular and well-documented. Clearly defining function inputs and outputs will ensure a smooth integration within applications.

Several platforms—like OpenAI, together AI, and more—now support function calling for AI development. Each platform has its own documentation and resources to explain how to define functions, specify their arguments, and integrate them into your application's workflow.

Use Telnyx to build powerful AI applications

At its core, function calling enables more precise and efficient interactions with various systems. This powerful capability allows developers to create smarter, more responsive AI that can handle complex tasks with ease. By incorporating function calling, businesses can streamline operations, reduce errors, and enhance the overall user experience, increasing satisfaction and productivity.

As AI continues to evolve, integrating function calling into your applications will become essential for staying competitive and innovative. Embracing this technology simplifies development and unlocks new possibilities for automation and problem-solving. Whether you're building customer service bots, optimizing logistics, or enhancing data analysis, function calling can significantly elevate your AI's performance.

Telnyx, a leader in connectivity and communication solutions, offers Telnyx Inference, a robust API for integrating advanced AI functionalities like function calling. To integrate function calling with our chat completions API users can follow our developer guide or reach out to our team in the portal.

With our expertise in real-time communication and global connectivity, Telnyx Inference ensures that your AI applications are smarter, more reliable, and scalable.

Take a look at our developers guide to integrate function calling with our chat completions API. Our extensive developer documentation will help you build AI applications to drive your business toward greater efficiency and innovation.

Share on Social

Related articles

Sign up and start building.