prompt caching

Hundreds of examples in a cached prompt can match fine-tuning and let you change behaviour in minutes. Price both, then decide by volume, latency and how often the examples change.

A short question with a large payoff. A PM who knows the one rule of prompt caching can cut a feature's input bill dramatically without touching quality.

The token bill drops by two-thirds and failures rise by one point. Price the failures before you call it a saving, and check what routing does to your cache.

Candidates jump to 'switch to a cheaper model'. The interviewer wants to see you find out why cost per user rose before choosing a lever, then pull the levers in the order that protects quality.

Caching is the cheapest large saving in most AI products, and a single line of personalisation at the top of the prompt can turn it into a 22% cost increase. The arithmetic is short; the layout insight is what scores.