Understanding AI Guardrails

AI guardrails serve as explicit validation and verification checks surrounding AI application calls, helping ensure reliability and safety. Unlike philosophical interpretations, guardrails in practical use focus on assessing the input and output of AI models to manage risks effectively. Shreya Rajbal emphasizes that the modern generative AI landscape requires these guardrails due to the high-stakes nature of its applications, where unbounded, unpredictable responses necessitate strict adherence to validation protocols. By implementing guardrails, developers can verify assumptions and minimize potential errors in AI outputs.