Power of Function Calling with LLMs: A Beginner-Friendly & Practical Guide | by Ranjeet Tiwari | Senior Architect – AI | IITJ | Jan, 2025
Imagine asking your AI assistant, “What’s the weather in New York?” and it responds by connecting to a weather API and providing real-time data.
That seamless experience is powered by function calling, an advanced technique enabling large language models (LLMs) like GPT-4 or Google Gemini to interact with external tools and APIs. By combining natural language understanding with practical functionality, function calling is transforming how LLMs integrate into real-world applications.
In my experience developing AI-driven solutions for industries like finance and banking, I’ve seen how function calling can unlock the true potential of LLMs. Let’s dive into how this works, explore real-world applications, and learn how to implement it. 🌟
Function calling allows LLMs to connect with external systems, APIs, and tools. By providing the model with predefined functions and their descriptions, the LLM can:
- Detect when a specific function needs to be called.
- Output a structured JSON with the required arguments.
- Enable seamless interaction with external tools for data retrieval, task execution, or content generation.
About The Author
