This post from the easy-learn-ai project explains prompt caching for large language models, using Anthropic's Claude Code as a case study. During multi-turn conversations, models traditionally re-encode the entire prior context every turn, making input (prefill) costs the dominant expense. Prompt caching marks a breakpoint in the request and stores the encoded prefix for reuse when the prefix matches exactly. Cached tokens cost about 10% of normal price (90% savings), while writing the cache costs 1.25x once; a 10-round long conversation can save roughly 90% of input costs and reduce time-to-first-token. Anthropic treats cache hit rate as an infrastructure-level metric with on-call incident response. The article covers key constraints (minimum 1024/4096 tokens, 5-minute TTL), prompt ordering best practices (static content first: system instructions, tool definitions, project docs, session context, messages), three pitfalls (changing prompts, switching models or accounts, modifying tool definitions), and advanced techniques including Plan Mode via message injection, Lazy Loading with tool placeholders, Cache-Safe Forking for compaction, and Sub-Agent isolation. It frames prompt caching as constraint-driven architecture.
When AI Learns Perfect Memory: How Prompt Caching Cuts LLM Inference Costs by 90%
Summary
This post from the easy-learn-ai project explains prompt caching for large language models, using Anthropic's Claude Code as a case study. During multi-turn conversations, models traditionally re-encode the entire prior context every turn, making input (prefill) costs the dominant expense. Prompt caching marks a breakpoint in the request and stores the encoded prefix for reuse when the prefix matches exactly. Cached tokens cost about 10% of normal price (90% savings), while writing the cache costs 1.25x once; a 10-round long conversation can save roughly 90% of input costs and reduce time-to-first-token. Anthropic treats cache hit rate as an infrastructure-level metric with on-call incident response. The article covers key constraints (minimum 1024/4096 tokens, 5-minute TTL), prompt ordering best practices (static content first: system instructions, tool definitions, project docs, session context, messages), three pitfalls (changing prompts, switching models or accounts, modifying tool definitions), and advanced techniques including Plan Mode via message injection, Lazy Loading with tool placeholders, Cache-Safe Forking for compaction, and Sub-Agent isolation. It frames prompt caching as constraint-driven architecture.
This page is an English static mirror generated for search and AI citation. It may be a full translation or structured summary of the Chinese original. Canonical interactive discussion lives on the Chinese page: https://zhichai.net/topic/177619891