Natural language to SQL (NL2SQL) is a critical capability for democratizing data access, but accuracy has long been a bottleneck. A new architectural approach from a Chinese AI company claims to push accuracy from 60% to 95% by treating RAG not as a single technique but as a multi-dimensional knowledge injection system. The framework comprises four layers: Schema RAG, which grounds the model in the database structure; Knowledge RAG, which injects domain-specific business rules; Few-shot RAG, which provides relevant examples; and Context RAG, which supplies conversational context. Each dimension addresses a different failure mode, from schema misalignment to ambiguous user queries. The article details the data structures and retrieval strategies for each layer, offering a blueprint for teams building their own NL2SQL systems. While the specific implementation is proprietary, the architectural principles are broadly applicable. For enterprises seeking to enable self-service analytics, this framework provides a compelling template for improving query accuracy and user trust in AI-driven data tools.
An in-depth look at a four-dimensional RAG framework (Schema, Knowledge, Few-shot, Context) that dramatically improves NL2SQL performance, with practical insights for enterprise AI teams.