Why LLMs Struggle with Text-to-SQL & How to Fix It

Large language models (LLMs) can generate SQL queries from natural language but require specific contextual knowledge to be effective, which includes understanding the schema, business terminology, and usage patterns of the data they work with. Text-to-SQL is challenging because it involves converting natural language into valid SQL that runs correctly on a data warehouse, necessitating thorough context in terms of schema, business, and usage. Select Star collaborates with data teams to address these challenges using strategies such as prompt engineering, fine-tuning, retrieval-augmented generation (RAG) pipelines, and Model Context Protocol (MCP) servers with AI agents. Each technique offers different levels of setup effort, flexibility, and suitability for various use cases, with MCP servers providing real-time access to contextual data without the need for retraining or manual prompt maintenance. The text emphasizes the importance of reliable metadata and governance strategies in ensuring that LLMs and AI agents can perform text-to-SQL operations effectively, thereby enhancing natural language interfaces in data analytics tools.

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