Prompt Robustness

A robust prompt works well across the messy variety of real-world user inputs — it doesn’t break when someone phrases things unusually, includes typos, mixes languages, or tries to push the model off its intended behavior. Robustness is distinct from accuracy: a prompt can produce excellent outputs on test cases but fall apart on the long tail of real usage. Building robustness typically requires testing with diverse and adversarial inputs, not just typical examples, and sometimes means writing more explicit instructions to handle edge cases rather than assuming the model will infer your intent. For behavior architects, prompt robustness is a key quality dimension — a prompt that’s deployed in production is only as good as it is across all the inputs it will actually receive.