Why Prompt Caching Matters for LLM Applications?Modern LLM apps often get the same, or almost the same, prompt more than once. This happens with form submissions, common FAQ questions, agent tool calls, or a user asking the same thing in different words. Without caching, each of these repeat requests pays full token cost, full latency, and adds unwanted pressure on your rate limits.There are four caching methods used in production today. This article, Part 1 of a 3-part series, covers the first and most basic one: exact-match caching. Part 2 covers semantic caching, for requests that mean the same thing but are worded differently. Part 3 covers what happens at the model-serving layer itself: self-hosted KV cache reuse and provider prefix caching, plus the production details every team ends up needing: watching your metrics, working out your costs, and a full checklist.