Stop Wasting Tokens: The Deep-Dive Guide to Prompt Caching
· 5 min read
If you’ve used terminal-based AI agents like Claude Code, Cursor, or custom multi-turn coding assistants, you’ve probably noticed something remarkable: they read entire codebases, file trees, git diffs, and project specs, yet consistently return fast responses across dozens of turns.
How do these tools feed tens of thousands of context tokens into a model on every single turn without causing massive latency spikes or huge API bills?
The underlying engine is prompt caching. Whether you’re building agentic coding workflows or optimizing AI APIs in production, prompt caching is a fundamental architectural pattern. It can cut API costs by up to 90% and slash Time-to-First-Token (TTFT) latency by 80%.