Structured query languages (SQL, SPARQL, etc.) are essential for retrieving information from

specialized databases, yet their complexity often poses barriers for non-expert users. Translating

natural language (NL) questions into accurate structured queries can democratize data access and

improve usability. This thesis explores the use of example-based prompting strategies with large

language models (LLMs) to facilitate NL-to-structured query translation. Specifically, it examines

how variations in the number, selection, and formatting of examples influence translation

accuracy, consistency, and robustness. The study aims to provide insights into optimal prompting

techniques for enhancing LLM performance in this domain.

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