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Data preparation for function tooling is boring
Lesson 2: The most boring part that you fail without
They might contain general instruction-following data, but not the kind of structured, fine-grained mapping from natural language to your laptop or phone’s API functions—like muting audio, setting an alarm, or opening a specific file. Once we’ve covered the basics, single-tool tasks, atomic behaviour, we begin to scale complexity by asking the model to handle instructions that require multiple tool invocations. In the next section, we shift gears and enter the quality control phase, including deduplication, format validation, and execution tests, which are essential for turning synthetic data into reliable supervision.
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