What are Fallbacks in AI Apps?

Modern software teams are emphasizing the importance of implementing effective fallback strategies in AI applications to enhance user experience and maintain trust even when AI systems encounter issues. Fallbacks serve as contingency plans that are activated when an AI application faces problems such as technical errors, low confidence in responses, or blocked actions by guardrails. Several strategies for fallbacks are discussed, including graceful degradation, deterministic approaches, human-in-the-loop interventions, model-to-model handovers, and static responses. These strategies aim to prevent undesirable outcomes like dead ends, hallucinations, and brand damage, ensuring that the AI app can handle unpredictable situations gracefully. Building effective fallbacks involves identifying trigger points, creating a chain of fallback options, ensuring smooth context hand-offs, and designing user-friendly interfaces. Regular testing, intelligent automation, and monitoring are recommended to validate the effectiveness of fallbacks, ultimately transforming potential system failures into seamless, alternative user pathways.