Why Text-to-SQL Needs Relationship Context, Not Just Better Prompts
Enterprise Text-to-SQL systems require more than just advanced language models; they need structured context about data relationships. Many current systems fail in production because they cannot infer the correct tables, metrics, and join paths from complex, ambiguously named enterprise databases. A more robust approach involves providing the language model with explicit relationship context, such as semantic mappings and confidence scores for joins, to ensure accurate and deterministic SQL generation. AI
IMPACT Enterprise Text-to-SQL systems will require robust data relationship discovery and semantic mapping to ensure accuracy and reliability beyond basic prompt engineering.