Prompt Caching for Lower Cost and Latency
Structure prompts so repeated prefixes are cached, then measure the savings in cost and response time.
A taste of a lesson
I added caching but usage shows zero cached tokens. My system prompt starts with today's date.
That date is probably the culprit. Caching matches an identical prefix from the very first token, so if the first line changes, nothing after it can match, at least across days, and per request timestamps break it every time. Move the date, user name and anything else that varies to the end, inside the user message. Keep tools, instructions and examples in a fixed order at the start. Then send two identical requests a few seconds apart. Do the cached token counts change?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain how prefix based prompt caching saves cost and latency
- Reorder prompt content so stable parts form a cacheable prefix
- Find and remove the details that silently break caching
- Measure cache hit rates and savings from usage fields
- Choose between prompt caching and caching whole responses
Lesson plan
- 1 What prompt caching reuses Understand that providers reuse processing of a repeated prompt prefix. Start
- 2 Ordering for caching Arrange request content from most stable to least stable. Start
- 3 What breaks the cache Spot the small changes that cause misses. Start
- 4 Measuring hits and savings Confirm caching works using usage fields and logs. Start
- 5 Where caching pays off Identify the workloads that benefit most. Start
- 6 Response caching in your app Decide when storing whole answers is safe and useful. Start
Try asking
About this tutor
For developers whose requests share large repeated parts: a long system prompt, tool definitions, a reference document asked about many times, or a growing conversation. You learn how provider side prompt caching works in general, why it depends on an identical prefix, how to order prompt content so the stable parts come first, what breaks caching, and how to confirm hits from usage fields. The tutor explains how providers differ (automatic versus explicit markers, minimum lengths, cache lifetimes, pricing of cache writes) without promising any one provider's details, and contrasts prompt caching with caching whole responses in your own app.
Reviews
4.5
2 ratingsSample
- Henrik S.Sample
Honest that provider details differ, and clear about what is general. The response caching warning about semantic caches was valuable.
- Rania F.Sample
Our system prompt had a request id at the top. Moving it to the end took our cached share from nothing to most of the input. The measuring lesson proved it.
About the teacher
Keeps LLM features fast, affordable and available as traffic grows: cost, caching, retries and observability
9 tutors 347 lessons taught Sample
Most of my working life has been on platform and reliability teams, and these days I spend it on LLM features: the bills that surprise people, the 429 errors on launch day, the logs nobody can read. I teach the operational side of building with model APIs. We estimate costs before writing code, add retries that do not make outages...
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