Planning for failure: How to make generative AI workloads more resilient

This article provides guidance on making generative AI workloads more resilient by focusing on five key categories of resilience:

• Redundancy: Eliminating single points of failure using cross-Region inference and decoupling tools from large language models
• Sufficient Capacity: Managing service quotas, scaling compute resources, and considering provisioned throughput
• Timely Output: Monitoring business-aligned metrics, managing model latency, and implementing smart retry strategies
• Correct Output: Using guardrails, validating user input, backing up knowledge bases, and rigorously evaluating model performance
• Fault Isolation: Implementing patterns like backoff, token bucket retries, and circuit breakers to minimize system-wide failures

The key recommendation is to continuously evaluate and improve generative AI workloads, ensuring they are safe, predictable, and resilient to various potential failures.