Lastly, the problem of model selection and domain expertise arises. Training large language models from scratch is expensive, and streamlining this process is an active field of research . Deployment and inference are less costly but still resource-intensive, creating an incentive to use smaller models at the cost of reduced performance and functional generalizability. Procuring a model wellsuited to the kinds of generation tasks a user hopes to solve is not straightforward, especially if the user wishes to rely on libraries using latent domain-specific knowledge not well-represented in the training data of off-the-shelf models, and which cannot be easily adapted using prompt tuning alone. For instance, models may perform well in programming competitions , but may not transfer well to the use of libraries operating with crystallography, such as Pymatgen defining space groups, crystal sites, listing symmetry operations, nor understand the complex contextual relationships between prompt and model output.