  Large language models (LLMs) with in-context learning have significantly
improved the performance of text-to-SQL task. Previous works generally focus on
using exclusive SQL generation prompt to improve the LLMs' reasoning ability.
However, they are mostly hard to handle large databases with numerous tables
and columns, and usually ignore the significance of pre-processing database and
extracting valuable information for more efficient prompt engineering. Based on
above analysis, we propose RB-SQL, a novel retrieval-based LLM framework for
in-context prompt engineering, which consists of three modules that retrieve
concise tables and columns as schema, and targeted examples for in-context
learning. Experiment results demonstrate that our model achieves better
performance than several competitive baselines on public datasets BIRD and
Spider.
