How do you handle fallback strategies in LLM systems?

Fallback strategies ensure system reliability by switching models, simplifying prompts, or returning cached responses when failures occur.

LLM systems are inherently probabilistic and external-service dependent. Failures may include API timeouts, hallucinations, or unsafe outputs. Fallback mechanisms include retry logic, switching to smaller models, cached responses, or deterministic rule-based systems. This ensures graceful degradation instead of full system failure.