I was debugging an API integration issue and every single query I made sent my entire codebase as context. That’s 50,000 tokens being processed every single time I asked a question.
The codebase wasn’t changing between queries, but I kept paying full price to send it over and over again with each new question.
Then I actually learned about prompt caching and everything changed. Send the codebase once, cache it on the server, and reuse it for hours. I was suddenly paying a tiny fraction of what I paid before for the exact same results, just way more efficiently. The weirdest part? Almost nobody knows this feature even exists.
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What Is Prompt Caching?
Prompt caching allows AI models to remember specific parts of your prompt on their servers so you don’t have to resend those parts with every single request.
Without caching: You send a 50,000 token codebase plus your question three separate times, which means the model processes 50,000 tokens three times, and you pay full price for processing all those tokens three times.
With caching: You send the 50,000 token codebase once where it gets cached on the server, then send only your new questions, which means the model processes the huge context exactly once and then only processes your new questions for subsequent queries, so you pay full price once for the big context and minimal costs for everything after.
How It Actually Works
The AI provider stores the expensive part of your prompt (like that 50k token codebase) directly on their servers. When you make a new query, their system pulls the cached content from storage, processes only your new question, and combines them together before generating a response.
The cache typically lives on their servers for a few hours, during which time you can reuse it over and over instead of reprocessing the same content repeatedly.
Real Use Cases
Debugging coding issues: Your codebase stays static while your debugging questions change constantly. Cache the entire codebase once, then ask 20 follow-up questions without resending all that code.
Document analysis workflows: You have a 100-page PDF and need to ask multiple questions about different sections. Cache the entire document once, then query it repeatedly without resending the PDF every time.
Customer service chatbots: You have company documentation that stays constant while customer queries vary wildly. Cache your docs once per user session, and all subsequent queries reference that cached documentation.
Code review sessions: You’re reviewing a large diff and have multiple questions about different sections. Cache the entire diff once, then ask targeted questions about specific sections without resending the whole thing.
How To Structure Prompts For Caching
The key is to always put your static content first in the prompt and your varying content second.
Bad structure that breaks caching: Question first, then context second. The API can’t cache anything because the cacheable part comes after the part that changes, which defeats the entire purpose.
Good structure that enables caching: Context first, then question second. The API can cache the static context part and only process your new questions each time, which is exactly what we want.
Which Providers Support It
Anthropic (Claude): Prompt Caching OpenAI (GPT-4): Internal only Google (Gemini): Context Caching
Check docs for API parameters.
Why Nobody Uses This
Prompt caching is buried deep in API documentation where nobody actually reads. It’s not advertised prominently anywhere on the marketing pages. You need direct API access to use it, so the web interface doesn’t expose this feature at all. You have to restructure your existing prompts to put static content first. It’s not automatic or enabled by default. And the documentation explaining how to use it is surprisingly sparse and hard to find.
When Caching Doesn’t Help
Caching provides zero benefit when you’re making single one-off queries where there’s nothing to reuse. It doesn’t help when your context is constantly changing between every request. Short contexts aren’t worth caching because the overhead of managing the cache costs more than just resending the small amount of content. Cross-session queries that span hours or days don’t benefit because the cache expires and you have to rebuild it anyway.
How To Implement It
First, identify what parts of your prompt are actually static and get reused across multiple queries. Second, restructure your prompt to put all that static content at the beginning. Third, mark that static section as cacheable using the appropriate API parameters from your provider’s documentation. Fourth, make your subsequent requests and verify they’re actually using the cache. Fifth, monitor your cache hit and miss rates to ensure it’s working as expected.
Cache Expiration
Caches typically expire after 1-2 hours of total time, after a period of inactivity, or when you manually trigger a refresh. If your cache expires mid-session, you just rebuild it once with your next query, and then all subsequent queries can use that new cache. Even with occasional rebuilds, this is still dramatically more efficient than never caching anything in the first place.
My Experience
I use caching extensively for debugging sessions where I’m working with the same codebase over and over, document analysis where I’m asking multiple questions about the same PDF or markdown file, and testing different prompt variations against the same large context.
I don’t bother with caching for one-off queries where I’ll never reuse the context, small contexts that are cheap to resend anyway, or constantly changing conversations where the context never stays stable long enough to benefit.
The sweet spot where caching absolutely shines is when you have a large static context combined with multiple queries over a relatively short time window.
The Weird Part
Incredibly useful but nobody talks about it. Most optimization advice: better prompts, lower temperature, smaller contexts. All good. But none addresses resending same massive context repeatedly.
Caching solves that. Not glamorous. Just practical infrastructure saving time and money.
Should You Use It?
You should absolutely use caching if you’re using APIs programmatically, sending large contexts repeatedly, asking multiple questions about the same information, or running high query volumes where costs actually matter.
You can skip caching entirely if you’re just doing casual ChatGPT web interface use, asking one-off questions that never reuse context, or working with small contexts that are cheap to resend anyway.
The Bottom Line
Prompt caching genuinely works and makes large context queries dramatically more efficient in both time and cost. But you actually need to know that it exists in the first place, restructure your prompts to put static content first, and explicitly enable it in your API calls.
Most developers don’t know about caching, so they keep resending 50,000 tokens with every single query and then wonder why their API bills are so absurdly high.
Now you know better than that. Cache your static content once, reuse it for hours, and watch your API costs drop. Your finance team will absolutely thank you.