Function Calling: LLMs Using Tools
Function calling turns an LLM into an API translator: it reads input and emits JSON telling your code which tool to run. Use it when the model needs live data it cannot store in weights. The model never executes the call and can hallucinate arguments.
Function calling turns an LLM into an API translator. Instead of answering directly, the model parses intent and emits JSON with a function name and arguments. Your code receives the payload, validates it, executes the function, and feeds the result back for a final response. Use it whenever the model needs live data, calculations, or external actions outside its training weights. Never assume the model executes the function; it only formats the request and will hallucinate arguments if your schema or prompt is ambiguous.
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