Is this more like text-to-semantic layer or does it throw the schema in the prompt and generate SQL with the llm?
- the engine keeps an updated catalog of the data (low cardinality columns, their values etc) - taps into query history and finetunes the model to the schema - allows uploading context from unstructured sources like docs and data dictionaries - has an agent which collects all relevant info, generate the SQL, tries to retrieve a few rows to recover from errors and provides an confidence score to the generated SQL