Infrastructure

Prompt Caching

Quick Answer: An optimization where the API provider stores the processed representation of frequently repeated prompt prefixes, avoiding redundant computation on subsequent requests.

Prompt Caching is an optimization where the API provider stores the processed representation of frequently repeated prompt prefixes, avoiding redundant computation on subsequent requests. When you send a request with a cached prefix, the provider skips reprocessing those tokens, reducing latency and cost.

Example

Your application sends a 3,000-token system prompt with every request. With prompt caching, the first request processes all 3,000 tokens normally. Subsequent requests within the cache window reuse the processed prefix, reducing time-to-first-token by 80% and input token costs by 50-90%.

Why It Matters

Prompt caching can dramatically reduce costs and latency for applications that reuse large system prompts, few-shot example sets, or document contexts. It's a practical optimization that prompt engineers should design for when building production systems.

How It Works

Prompt caching works by storing the key-value cache (the internal representation computed during the transformer's forward pass) for a prompt prefix. When a new request starts with the same prefix, the provider loads the cached representation instead of recomputing it. This saves both computation time and processing costs.

Different providers implement caching differently. Anthropic's prompt caching lets you explicitly mark cache breakpoints in your messages, with cached input tokens priced at 10% of regular input tokens. OpenAI automatically caches prompt prefixes longer than 1,024 tokens, with cached tokens at 50% of regular price. Google's Gemini offers context caching for frequently reused contexts.

To take advantage of prompt caching, structure your prompts with static content first (system instructions, few-shot examples, reference documents) and dynamic content last (the user's specific query). The more tokens you can keep identical across requests, the more you benefit from caching. This is one reason why separating system prompts from user inputs isn't just good practice for clarity; it's an optimization strategy.

Common Mistakes

Common mistake: Putting dynamic content before static content in prompts

Structure prompts with static system instructions and examples first, dynamic user content last. This maximizes the cacheable prefix.

Common mistake: Not knowing your provider's caching behavior and pricing

Read your provider's documentation on prompt caching. Different providers have different minimum token thresholds, cache durations, and pricing.

Common mistake: Assuming caching works across different conversations or sessions

Prompt caches typically expire after a time window (5-60 minutes depending on provider). Design your application to send requests frequently enough to keep the cache warm.

Career Relevance

Prompt caching knowledge demonstrates cost-consciousness and production-readiness. Senior AI engineering roles expect you to optimize for both performance and cost, and caching is one of the most impactful optimizations available.

Related Terms

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