How does function calling work with large language models?
The Step-by-Step Process
First, you provide the model with a list of functions, including their names, descriptions, and parameter schemas (often in JSON Schema format). This tells the model what tools are available. When a user sends a message, the model analyzes it and decides whether to call a function. If so, it returns a structured object with the function name and arguments instead of a normal text reply.
Your application then parses that object, runs the actual function (e.g., an API call), and sends the result back to the model as a new message. The model uses that result to craft a natural language response to the user. This back-and-forth can happen multiple times in a single conversation.
Under the Hood
Models that support function calling are typically fine-tuned to recognize when a function is needed and to output valid JSON. The model doesn't execute anything; it just predicts the next tokens, which happen to form a function call. The reliability depends on the model's training and the clarity of your function descriptions.
Some platforms, like OpenAI's API, have built-in support for function calling. Others may require you to use prompt engineering to achieve similar results. The key is that the model must understand the function's purpose from its description.
- Define functions with clear names and descriptions
- Provide parameter schemas to guide the model
- Model returns a function call in JSON
- Your code executes the function
- Send the result back to the model for a final answer
Common mistakes
- Forgetting to include the function result in the next model call, leaving the model unaware of what happened.
- Writing vague function descriptions, which leads to incorrect or missed calls.
- Assuming the model will always call the right function; you need error handling and validation.