A new hybrid framework combines large language models with thermodynamic databases to improve the synthesis planning of inorganic materials, particularly in the niobium-oxygen system. This approach shows that LLMs can generate more viable synthesis routes compared to traditional algorithms, highlighting their potential in materials science.