In this work, we take inspiration from the work of SAMOA and the use of prompts to condition language generation as in models like GPT . More specifically, we propose to leverage the implicitly learned relationship between molecular sub-structures (as groups of SMILES tokens) by a simple unidirectional decoder-only model when trained on a corpus of molecules. This can be achieved by providing a molecular substructure as a prompt at inference time to condition further molecule generation (and constrain the final molecule to contain this sub-structure). Further, the CLM can adapt to this task and optimize for different objectives when combined with RL. In the case of scaffold decoration of more than one attachment point, prompt-based generation is repeated for each attachment point (integrating any previously generated de novo decorations at each iteration). In the case of fragment linking, one fragment is chosen as the prompt and further fragments are inserted into the de novo generated sequence and evaluated by the CLM.