Large chemical language model outputs are tunable via prompt engineering. There is a vast space of potential applications for a pre-trained LLM for chemistry, including but not limited to downstream tasks like property prediction and distribution learning . Here, to demonstrate potentially unique capabilities of ChemGPT, we focus on two applications: 1) prompt engineering and 2) representation learning. Prompt engineering is an emerging field in generative LLMs, wherein training a model is only the beginning of the process, and the "quality" and diversity of outputs is tunable based on the sampling strategy and sequences used to condition the generation. In contrast to smaller, simpler generative models, the outputs of GPT-style models are highly tunable. The goal here is not to exhaustively investigate prompt engineering and representation learning, which have massive scope, but instead to demonstrate interesting capabilities of ChemGPT and provoke new research questions.