Stop Obsessing Over Prompt Engineering: Data Preparation is Key for AI Agents
2025-05-16
This article delves into the crucial, often overlooked aspect of building AI agents that call functions: data preparation. The author argues that prompt engineering alone is insufficient, highlighting that 72% of enterprises now fine-tune models instead of relying on RAG or building custom models from scratch. A detailed architecture for building a custom dataset is presented, encompassing defining a tool library, generating single-tool and multi-tool examples, injecting negative examples, and implementing data validation and version control. The importance of data quality is stressed throughout. The ultimate goal is a Siri-like AI system that understands natural instructions and accurately maps them to executable functions.
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