Intrinsic terminators are natural sequences in prokaryotes that cause transcription termination without additional proteins. They function by forming stable stemloop structures followed by a uracil-rich sequence, disrupting the transcription process. Designing synthetic, switchable terminators—those that can be activated or disrupted by small RNAs—has exciting applications in synthetic biology, such as precise control of gene expression. This project integrates computational modeling, artificial intelligence (AI), and experimental validation to create such terminators within an iterative design-build-test-learn (DBTL) framework.