Implement prompt caching correctly: how prefix caching works, structuring prompts for cache hits, measuring hit rate, invalidation, and the mistakes that defeat it.
Prompt caching implementation reduces the cost and latency of repeated prompt content by reusing processed context across requests. The saving can be substantial for systems with large stable prompts, and it is frequently left on the table because prompts are structured in a way that prevents cache hits.
How prefix caching works
Providers that offer caching store the processed representation of a prompt prefix. When a subsequent request begins with the identical prefix, that portion is served from cache at reduced cost and latency.
The critical property is that matching is on an exact prefix. Any difference near the start of the prompt invalidates everything after it, no matter how small.
This single fact determines the entire implementation approach: stable content first, variable content last.
Structuring prompts for hits
Order content by volatility. System instructions, tool definitions, few-shot examples, and any large reference material that rarely changes go at the beginning. User input, retrieved documents, and conversation state go at the end.
The common mistake is placing a timestamp, session identifier, or user name near the top of the system prompt. That one variable token invalidates the cache for every request, and the effect is invisible unless hit rate is measured.
Keep the stable section byte-identical between requests. Regenerating a system prompt through string formatting that produces slightly different whitespace defeats caching while looking correct.
Where a provider requires explicit cache markers, place them at the boundary between stable and variable content, and keep that boundary consistent.
What to cache
Large system prompts with detailed instructions and examples are the strongest candidates.
Tool definitions in agent systems, which are often substantial and identical across every call in a session.
Reference documents that many requests share, such as a policy manual or product catalogue used across a support workflow.
Long conversation prefixes in multi-turn sessions, where each turn repeats everything before it.
Small prompts are not worth caching, since the overhead exceeds the saving.
Measuring effectiveness
Track cache hit rate as a first-class metric. Without it, a change that quietly breaks caching goes unnoticed until the bill.
Track cost per request before and after, and latency at your percentiles, since caching typically improves time to first token noticeably.
Alert on hit rate dropping. A prompt edit that moves a variable earlier is a routine change with a large cost effect, and hit rate is the only signal that catches it quickly.
Invalidation and lifecycle
Cached entries expire after a provider-defined period, so caching helps sustained traffic more than sparse traffic. For low-volume systems the entry may expire between requests, producing little benefit.
When you change a system prompt, expect a period of misses while the new prefix populates. Deploy prompt changes with that cost in mind rather than reverting on a temporary spike.
Be careful with cached content containing tenant-specific data. Sharing a cached prefix across tenants is a data boundary question, so keep tenant-specific material in the variable section.
Frequently asked questions
How much does caching save?
For systems with large stable prompts and steady traffic, the reduction on cached input tokens is significant. Systems with small or highly variable prompts see little.
Does caching affect output quality?
No. It reuses processed context; the model behaves identically.
Why is our hit rate low?
Almost always variable content near the start of the prompt, or the stable section not being byte-identical between requests.
Can we cache across users?
Only content that is not user-specific. Keep anything tenant or user-scoped after the cache boundary.
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Written by
Moueen Togarvi
Founder & CEO at Voquarn Code, focused on product engineering, search growth, and practical AI systems.