prompt caching

Most teams reach for a token-dropping compressor and pay an accuracy tax for tokens they could have had for almost nothing. The ranking of levers matters more than any single technique, and the top of the list is not compression at all.

Every request pays for the prompt and almost nobody has looked at where the tokens actually go. It is rarely the part you can read: thirty tool schemas and seven stale examples usually outweigh the system prompt, and the output side costs several times more per token.

Agent latency is a step-count problem disguised as a speed problem. The winning move is removing round trips, not shaving milliseconds off each one, and knowing which step you can delete is the whole interview.

Caching is one of the biggest LLM cost levers, but 'cache the response' is naive for a non-deterministic system. What counts is telling prompt (prefix) caching apart from semantic caching and knowing when each is safe.