Building Reliable Text-to-SQL Agents with LLMs
A practical look at building text-to-SQL chat agents that return trustworthy answers, covering the full path from query generation to turning tabular results back into clear natural language, plus how to evaluate them.
Generative AI is changing how people interact with data, and text-to-SQL interfaces are becoming a popular way to query the growing amount of information companies store in databases. This talk walks through the end-to-end process of building text-to-SQL chat agents and the real challenges of getting reliable results from large language models. It goes beyond just generating SQL to look at often-overlooked steps, like converting tabular query results into natural language, where information loss and errors frequently creep in. Expect practical insights, the key pitfalls to watch for, and strategies for evaluating these agents effectively, including newer methods inspired by recent research that improve alignment with human judgment while reducing computational overhead.
What you will take away:
- How text-to-SQL chat agents work end to end, and where LLMs fit in
- Common pitfalls in generating SQL and translating tabular data into natural language
- Evaluation strategies for ensuring meaningful, accurate responses from LLMs working with tabular data