For GPT-3, inverse design is as simple as training the model with question and completion reversed. That is, answer the question What is a photoswitch with transition wavelengths of 324 nm and 442 nm, respectively with a text completion that should be a SMILES string of a meaningful molecule. This approach should be contrasted with the approach used by , in which a library of molecules is generated, and their ML model (a Gaussian process regression) is used to evaluate the transition wavelengths of each material. If one has a lot of knowledge about the system, one can design large specific libraries that contain many promising molecules, including molecules with transition wavelengths of 324.0 nm and 442 nm. But, such a brute force technique is not what we understand as inverse design, as it, by definition, cannot predict a molecule we did not include in our library.