Traditional reliability meant consistency. Given identical inputs, systems produced identical outputs. Costs were stable and behavior predictable. Inference reliability on the other hand is shaped by nondeterminism. Outputs vary due to stochastic generation, retraining introduces drift, and token-based billing can cause cost fluctuations. The new dimension of reliability is semantic consistency, that is, the ability to deliver outputs of acceptable quality, accuracy, and predictability over time despite probabilistic behavior.

In this episode of Pop Goes the Stack, F5's Lori MacVittie and Joel Moses are joined by guests Ken Arora and Kunal Anand as they dive into the topic of reliability in AI systems. They explore the concept of 'slop' (AI variability) as a potential feature rather than a bug, discuss the importance of contextual semantic consistency, and weigh guardrails and evals tailored to specific inference workloads. Tune in to learn how to navigate the evolving AI landscape and take note of practical tools and strategies like multi-model chaining, distillation, and prompt engineering to ensure reliability.

Find out more in the blog How AI inference changes application delivery: https://www.f5.com/company/blog/how-ai-inference-changes-application-delivery