Fine-tuning for structured output and tool calling: data, grammars and an evaluation harness
How to get a model to reliably emit JSON and tool calls: what prompting, constrained decoding and fine-tuning each guarantee, how to design call, no-call and clarify examples, and a runnable validator and metric harness that separates valid JSON, valid schema, the right tool, exact arguments and false calls.
Applications that use a model rarely want prose. They want JSON to store, arguments to pass to a function, or a decision to act on. A model that is right 90% of the time but returns something unparseable the other 10% breaks the program. This lesson covers the three ways to get reliable structure, how to build fine-tuning data for tool calling, and an evaluation harness you can run today. It connects to structured output and function calling and agent and MCP security.
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