When does caching help an LLM product and when does it hurt?

Example Answer

The way I'd think about it is this: Caching helps when requests repeat enough, freshness requirements are manageable, and correctness is preserved by reuse. That is common for stable prompts, repeated retrieval results, deterministic transformations, and frequently asked low-personalization queries.

It hurts when personalization, freshness, or subtle context differences matter more than reuse. Then a high cache hit rate can actually hide stale or inappropriate outputs while making dashboards look efficient.

I like caching when I can define a clear correctness boundary. If I cannot explain when a cached result is still valid, I am probably caching too aggressively.

What matters in an interview is not only knowing the definition, but being able to connect it back to how it changes modeling, evaluation, or deployment decisions in practice.

Common Poor Answer

A weak answer is saying more caching is always good because tokens are expensive. Cost savings do not help if cache correctness is weak.