Data preparation for function tooling is boring

a year ago

Data preparation for function tooling is crucial but often overlooked.
Fine-tuning is necessary for task-specific applications like function calling, requiring 10k-100k high-quality examples.
RAG (Retrieval-Augmented Generation) is suitable for context-retrieval tasks but not for structured reasoning tasks like function calling.
Function calling requires behavior generation, not just knowledge retrieval.
Dataset preparation starts with defining the function library and the interface between natural language and executable code.
Generating a dataset involves single-tool examples, multi-tool examples, unknown intents, and paraphrasing for linguistic diversity.
Validation includes deduplication, format validation, and execution testing to ensure correctness.
MLOps principles like dataset versioning, cost tracking, and health reports are essential for maintaining dataset quality over time.
The next steps involve fine-tuning a small model on the prepared dataset and evaluating its performance.

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