This LLM-based approach showed flexibility in separating the seed and growth subrecipes within a seed-mediated growth recipe. In contrast, there were cases where a straightforward separation algorithm utilizing the transition keyword (e.g. "For growth solution") failed, resulting in the loss of a considerable number of relevant recipes. Also, the LLM showed flexibility in extracting precursor amounts. Specifically, the LLM could extend the predefined set of precursors of interest (such as 5-BrSA, Triton X-100, SDS, LSB and NH2OH which were not included in the original precursor set for regex), while considering the multitude of organic reagents, including all of the possible precursors to a list of regular expressions can easily be an arduous process. Furthermore, the LLM could also easily adapt to out-ofdistribution presentations of the precursor amounts. This was particularly interesting as we initially expected the patterns of the amounts of a chemical to be very limited, and regarded the others as the edge cases. For example, the LLM could also correctly parse amounts from the phrase "a mixture of (1 mL, 2 mM) HAuCl4 and (3 mL, 4 mM) CTAB" which we did not include in our search-based detection and linking for the precursors.