Scaling Enterprise-Grade RAG: Lessons from Legal Frontier - Calvin Qi (Harvey), Chang She (Lance)

Building effective Retrieval-Augmented Generation (RAG) systems for the legal sector requires addressing significant challenges in data scale, query complexity, and domain-specific jargon. Successful implementation relies on a tiered evaluation strategy, balancing automated quantitative metrics with expert-led qualitative reviews to ensure retrieval accuracy. Beyond algorithmic refinement, infrastructure must support multimodal data, including large documents and diverse file types, while maintaining strict security and privacy standards. LanceDB facilitates these requirements through an AI-native multimodal lake house architecture that separates compute and storage, allowing for efficient, large-scale indexing and retrieval. By treating data as a unified source of truth, teams can streamline feature engineering and model training, ultimately enabling faster iteration cycles in a rapidly evolving technological landscape.