Converting XQuery to SQL with Local LLMs: Do I Need Fine-Tuning or a Better Approach?

The challenge of converting XQuery statements to SQL queries in enterprise environments while using locally-run LLMs. The main obstacle is limited training data—finding enough diverse pairs of XQueries and their SQL equivalents to properly train a model. The author initially explored a parsing-based approach to extract key elements like table names, columns, and conditions from XQuery statements, but encountered limitations. The post explores whether fine-tuning a local LLM is necessary or if alternative strategies (like prompt engineering, retrieval-augmented generation, or hybrid parsing methods) might be more practical given the data constraints.