NLP2SQL: Natural Language Database Querying
Building a system that translates natural language questions into SQL queries for intelligent data access.
What needed to be solved
Business users needed to query complex databases without writing SQL, but existing solutions couldn't handle domain-specific terminology and complex joins.
How we built it
Built a fine-tuned language model combined with a schema-aware retrieval system that translates natural language into accurate SQL queries with 94% accuracy on production workloads.
System Architecture
High-level architecture diagram showing the system components and data flow.
NLP2SQL Architecture
Lessons Learned
Schema understanding is critical - the model needs deep knowledge of table relationships
Query validation before execution prevents dangerous operations
User feedback loops dramatically improve accuracy over time
Complex joins require multi-step reasoning, not just pattern matching