To leverage AI, chemists need to ask the right questions

Chat applications such as ChatGPT, Claude, and Gemini that are based on large language models (LLMs) are artificial intelligence technologies that are already being used by chemists for tasks ranging from literature review and hypothesis generation to regulatory interpretation and toxicological analysis. These systems can summarize complex papers, suggest novel molecular combinations, and even simulate experimental outcomes. In short, they’re accelerating the pace of discovery. This kind of technology is no longer just a tool in the chemist’s toolbox—it’s becoming a research collaborator.I’ve been exploring how LLM-based chats are reshaping the scientific discovery process in chemistry. In a recent study I presented at the American Chemical Society 2025 Fall meeting, my coauthors and I discussed the pitfalls of this transformation. As AI systems evolve from passive assistants to active cocreators, we must rethink how we interact with them. And how we ensure their outputs are trustworthy, ethical, and scientifically sound.In the aforementioned study, chemistry experts described their interactions with LLMs as a mix of curiosity, trial and error, and cautious optimism. They appreciated the speed and convenience but remained wary of overconfidence and lack of nuance in AI-generated responses. One of the most surprising findings from our research was how much effort goes into simply asking the right question. Prompt engineering, the art of crafting inputs to get meaningful outputs from AI, is now a critical skill. Yet most chemists and experts in other domains aren’t trained in it. They rely on intuition, iteration, and a bit of luck.As one