Molecular photoswitches are organic molecules with extended aromatic systems that make them responsive to light. Upon radiation, they switch reversibly between different isomers (which changes some properties, such as dipole moments). This reversible switching makes them interesting molecules for applications ranging from sensing to drug discovery. These molecules are complex, making sufficiently accurate predictions using first-principles theory very expensive. Yet, it is important to have some guidance to identify promising molecules, and machine learning models have been developed for this. One of the important properties of these photoswitches is the wavelength at which there is a maximum in the adsorption spectrum for the E and Z isomer. Hence, we fine-tuned GPT-3 with the same data used by Griffiths et al. . As we have shown above, we can fine-tune GPT-3 to accurately answer questions like What is the pi-pi* transition wavelength of CN1C(/N=N/C2=CC=CC=C2)=C(C)C=C1C?".