Build fluency in the vocabulary of letting a model call a defined function with structured arguments.
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At standup, a dev mentions letting a model decide to call a defined function, like a weather lookup, with structured arguments, rather than only returning free-form text. What is this capability called?
Function calling, or tool use, lets a model decide to call a defined function with structured arguments, such as a weather lookup, rather than being limited to producing only free-form text. A model limited to plain text can't reliably trigger a real action or fetch live data on its own. This structured calling is what lets a model actually act on the world instead of just describing what it thinks should happen.
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During a design review, the team wants the model's proposed function call validated against a strict JSON schema before the application actually executes it. Which capability supports this?
Schema validation checks a model's proposed function call against a strict JSON schema before the application actually executes it, catching a malformed or unexpected argument. Executing every proposed call with no validation risks passing an invalid argument straight into a real function. This validation step is what keeps tool use safe even when the model's output isn't perfectly formed.
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In a code review, a dev notices a function's description and parameter names are written specifically to reduce the model's chance of choosing the wrong tool for a given task. What does this represent?
Tool description design writes a function's description and parameter names specifically to reduce the model's chance of picking the wrong tool for a given task. Leaving descriptions vague makes it more likely the model confuses two similar tools or misuses a parameter. This careful description work is a core, often underrated part of building a reliable tool-use system.
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An incident report shows a model called a destructive delete function with a wildly incorrect argument because no validation step caught the malformed input before execution. What practice would prevent this?
Validating a proposed function call's arguments against a schema, and requiring confirmation before a destructive action, catches a malformed input before it reaches a real, irreversible function. Executing every call immediately with no check risks exactly this kind of incident. This combination of validation and confirmation is essential wherever tool use can trigger a destructive or hard-to-reverse action.
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During a PR review, a teammate asks why the team validates every proposed function call instead of trusting the model to always produce a correctly formed call. What is the reasoning?
A model can still produce a malformed or subtly wrong function call, since it has no guaranteed awareness of every edge case a real function needs to handle safely. Validation catches that mistake before it reaches a real, possibly destructive function. The tradeoff is the added engineering work of writing and maintaining a strict schema for every callable tool.