Three key actions to improve accuracy
According to the running mechanism of the tool, the accuracy rate mainly depends on the schema quality and questioning method, and the following optimization scheme is recommended:
- Schema Refinement Strategy::
1) SQL users should include complete CREATE TABLE statements, especially for primary and foreign key relationships
2) MongoDB users need to specify the field type in JSON (e.g. {"user_age": "number"})
3) Add field comments to improve AI understanding, e.g. "# User Registration Channel ID". - Questioning Optimization Tips::
1) Include specific filters (e.g. "customers from NY in Q2 2023″ is better than "some customers")
2) Clarify output fields ("show name,email of..." instead of "show details")
3) Match schema with standard terminology (use "registration_date" instead of "signup time") - iterative verification method: After the first generation, a second comparison is generated by fine-tuning the keywords of the question (e.g., changing "recent" to "last 7 days").
Note: Complex queries are recommended to be split into multiple simple questions generated step by step, and then manually combined and optimized.
This answer comes from the articleText2Query: a tool for generating SQL and MongoDB query statements using natural languageThe
May not be reproduced without permission:AI productivity tools " How to optimize the accuracy of the query statements generated by Text2Query?
