How to Prompt LLMs for Text To SQL

This session features Shuaichen Chang, now an Applied Scientist at AWS AI Lab and author of the text-to-sql paper making the rounds. Shuaichen’s research (conducted at the Ohio State University) investigates the impact of prompt constructions on the performance of large language models (LLMs) in the text-to-SQL task, particularly focusing on zero-shot, single-domain, and cross-domain settings.

Shuaichen and his co-author explore various strategies for prompt construction, evaluating the influence of database schema, content representation, and prompt length on LLMs’ effectiveness. The findings emphasize the importance of careful consideration in constructing prompts, highlighting the crucial role of table relationships and content, the effectiveness of in-domain demonstration examples, and the significance of prompt length in cross-domain scenarios.

See the full transcript and more on the blog: https://arize.com/blog/how-to-prompt-...