Shopify의 데이터 구조화 여정: One-Shot LLM에서 DSPy 기반 에이전틱 아키텍처로

Shopify's transition from one-shot LLM prompting to a specialized multi-agent architecture powered by DSPy and self-hosted Qwen models demonstrates that domain-optimized smaller models can drastically outperform large language models in both cost and quality—reducing expenses by 75x while doubling extraction accuracy. This validates the strategic shift from manual prompt tuning toward automated optimization frameworks and proper system architecture as the path to sustainable performance. The case illustrates that specialized agent separation of concerns beats monolithic approaches, making this a key blueprint for enterprise LLM deployment optimization.