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Prompt Caching Explained With a Real API Example

What prompt caching means in practice

Prompt caching is a way for an AI provider to reuse the expensive part of a request when the same prompt prefix is sent again. Instead of re-processing your entire long system prompt, policy text, tool instructions, and other repeated context from scratch every time, the provider stores a cached version and reuses it on later requests.

In plain English: if your app keeps sending the same 8 KB of instructions and only the last user message changes, prompt caching can make those repeated requests faster and cheaper. This is especially useful for chat apps, coding assistants, RAG workflows, and any product with a stable “base prompt.”

How it works step by step

Most prompt caching systems work on the prefix of the prompt. The model sees a request like: fixed instructions first, dynamic user content later. If the fixed part matches a previously cached prefix, the provider skips part of the expensive prefill work.

• 1. First request: Your app sends a long prompt. The provider processes it normally and may create a cache entry for that exact prefix.
• 2. Second request: You send the same prefix again with new user input at the end.
• 3. Cache hit: The provider reuses the cached prefix instead of recomputing it, which reduces latency and token cost for the repeated portion.
• 4. Cache miss: If the prefix changes, even slightly, the cache may not match and the provider falls back to a full pass.

The key detail is that caching is usually exact-match or near-exact-match for the repeated prefix. Tiny changes in whitespace, tool definitions, or system instructions can invalidate the cache. That is why stable prompt design matters.

A hands-on example with a coding assistant

Imagine you are building a repo assistant that always sends the same product rules, coding style, and safe-completion guidance. Only the user’s question changes. A request through 59API might look like this conceptually:

POST https://api.59api.com/v1/chat/completions

{
"model": "claude-sonnet-4",
"messages": [
{"role": "system", "content": "You are a senior TypeScript assistant. Follow the repo rules: use strict typing, avoid any, keep functions small, and prefer existing utilities."},
{"role": "user", "content": "Add pagination to the orders endpoint."}
]
}

On the next request, the system message stays the same and only the user message changes. If the provider supports prompt caching for that model and request shape, the repeated system instructions become a good cache candidate.

Here is the same idea using the OpenAI SDK style, which 59API supports because it is fully compatible with OpenAI SDKs and tools like Claude Code and Codex:

import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.API_KEY,
baseURL: "https://api.59api.com/v1"
});

const response = await client.chat.completions.create({
model: "gpt-4.1",
messages: [
{ role: "system", content: "You are a concise code reviewer. Use the project conventions." },
{ role: "user", content: "Review this function for edge cases." }
]
});

Why prompt caching matters for cost and speed

Prompt caching is valuable when your prompts are long and repetitive. It can reduce the amount of work the model must do before generating the answer, which often means:

• Lower cost: You avoid paying full price for the repeated prefix on supported requests.
• Lower latency: Responses can start sooner because the provider reuses prior computation.
• Better scaling: High-volume apps with large templates or instructions become more economical.

This is where 59API is a practical choice. It gives developers cheap, pay-as-you-go access to native official-quality Claude and GPT models, without the usual markup pressure that makes experimentation expensive. If you are testing prompt caching strategies, a low-cost relay like 59API lets you run more real traffic, compare request patterns, and measure savings without committing to a large spend upfront.

Best practices to get more cache hits

• Keep the prefix stable: Put reusable instructions first and avoid editing them unless necessary.
• Move changing content to the end: User questions, file diffs, or search results should come after the static prefix.
• Avoid accidental churn: Changing punctuation, timestamps, or formatting in the system prompt can break cache reuse.
• Reuse templates: Keep one canonical prompt template for each workflow.
• Measure token usage: Watch repeated requests to see whether your cost and latency drop over time.

If you are already building with Claude or GPT models, prompt caching is one of the easiest optimization wins to test. Start with a stable prompt template, send a few repeated requests through 59API at https://api.59api.com, and compare cost and latency before and after. If the numbers look good, you will have a faster app and a smaller bill, plus a referral rebate if you share the platform with other developers.

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