Overcoming data scarcity and sparsity 32 is critical for data-driven predictions in materials synthesis. In this paper, we presented a hybrid approach for synthesis extraction, mixing conventional rule-based search methods with a fine-tuned LLM. By adding the LLM we were able to obtain about 50% more data compared to when only using the search-based method.