Twelve quick tips for designing AI-driven HPC workflows

Scientific computing has traditionally relied on predictable, linear pipelines. AI is disrupting that model entirely, introducing iterative, probabilistic processes that behave very differently from classical workloads. Researchers in genomics, climate science, drug discovery, and astrophysics increasingly need to run large foundation models alongside traditional simulations, but the infrastructure assumptions rarely match. This practical guide bridges that gap, offering concrete architectural advice on containerization, job scheduling, and data handling. Whether optimizing protein folding pipelines or training large models on cluster hardware, the tips here help research teams avoid common bottlenecks and build workflows robust enough to support the next generation of AI-driven science. Authors: Jamie J. Alnasir Paper: https://arxiv.org/abs/2606.07491v1