How LLM Function Calling Works: Structured Outputs via Constrained Token Generation

Large language models cannot browse the internet or natively return structured data, but function calling allows them to invoke external tools like APIs in a controlled way. Unlike plain text or basic JSON mode, function calling lets developers define an exact output schema — including field names, types, enums, and required fields — that the model must follow. This works through constrained decoding, where the API restricts which tokens the model can generate at each step to ensure the output matches the specified schema. As a result, function calling is the most reliable of the three main LLM output methods, eliminating the fragile parsing required with free-form text responses. A single user query can trigger multiple sequential tool calls, enabling the model to orchestrate complex, multi-step answers within one interaction.