Prompt Caching (Prompt Catching)

When you send a prompt to an AI model, the system processes every single token (word or word fragment) in your input before generating a response. For applications that reuse the same instructions or context repeatedly, this means the model does identical work over and over again.

Prompt caching solves this by letting the provider:

• Identify a static portion at the beginning of your prompt (your system instructions, style guidelines, or reference documents).
• Store the model's internal processing of that portion after the first request.
• Reuse that stored state on future requests that share the same prefix, so only the new parts—like a fresh user question—need processing.

Think of it like bookmarking your place in a long book instead of re-reading from page one every time you sit down.

Prompt caching can reduce response latency by up to 85% and cut input token costs by up to 90%, depending on the provider. For teams building chatbots, document analysis tools, or any application that repeatedly sends large context blocks to an AI model, this translates directly into faster user experiences and significantly lower operational costs. Understanding how it works helps you design smarter, more efficient AI workflows from the start.

Most major AI providers now support prompt caching, and in many cases it works automatically:

• OpenAI enables it by default on newer models like GPT-4o—no code changes needed.
• Anthropic (Claude) offers both automatic caching and explicit cache breakpoints you can set in your API calls using cache_control.
• Amazon Bedrock lets you define cache checkpoints and uses a time-to-live (TTL) window to manage cache expiration.

To get the best results, structure your prompts so static content comes first (system instructions, reference documents, boilerplate) and dynamic content comes last (the user's specific question). Avoid editing the static prefix unnecessarily—even small changes can invalidate the cache and force full reprocessing.

Imagine you're building a customer support chatbot for an online store using Promptitude. Your setup includes:

Static prefix (cached):

"You are a friendly customer support agent for GreenLeaf Shop. Follow these policies: [2,000 words of return policies, shipping guidelines, and product catalog details]. Always respond in a warm, professional tone. Never make promises about refunds without checking order status first."

Dynamic suffix (processed fresh each time):

User: "Hi, I ordered a plant pot last week and it arrived cracked. What can I do?"

Without prompt caching: The model processes all 2,000+ words of policies plus the user message on every single customer interaction—hundreds of times per day.

With prompt caching: The model processes those 2,000 words once, stores the result, and on every subsequent customer question, it only processes the new user message. The policies are already "understood" from the cache.

‍