prompt-caching

Prompt Caching - LLM Caching Optimization Expert

Skill Overview

Prompt Caching is an expert focused on LLM caching strategies. By designing multi-layer caching, they can reduce the cost of LLM calls by 90%, covering various optimization techniques such as prompt prefix caching, full response caching, and semantic similarity matching.

Suitable Scenarios

High-Concurrency LLM Applications

When an application needs to handle a large number of similar requests, caching the prompt prefix and response results can significantly reduce repeated token charges and substantially lower API costs.

Long Conversation Scenarios

In multi-turn conversations, the system prompt and context prefix typically remain unchanged. Using Anthropic’s native prompt caching can avoid repeated charges and improve response speed.

Document Q&A Systems

Using a CAG (Cache Augmented Generation) approach, pre-cache knowledge base documents into the prompt to replace traditional RAG retrieval, achieving faster responses and lower latency.

Core Features

Anthropic Prompt Caching

Leverage Claude’s native prompt caching feature to automatically identify and cache repeated prompt prefixes. This reduces token consumption and is especially suitable for scenarios containing large amounts of system prompts or contextual information.

Response Caching & Semantic Matching

Cache the full LLM response for identical or similar queries. Combined with semantic similarity algorithms, cache hits can occur even when requests are not exactly the same, further improving the hit rate.

Cache Augmented Generation (CAG)

Pre-cache commonly used documents directly into the prompt rather than performing RAG retrieval every time. This is suitable for knowledge bases with a modest document size but frequent access.

Cache Invalidation Strategy Management

Provide intelligent cache expiration and update mechanisms to ensure cached content remains current, avoiding returning outdated or incorrect information.

Frequently Asked Questions

What is prompt caching, and how does it reduce LLM costs?

Prompt caching is a technique that reduces repeated billing by caching the repeated parts of prompts (such as system prompts and context prefixes). Anthropic supports native prompt caching; when the same prefix appears again, you can avoid being charged for those tokens again. For requests that contain a lot of fixed content (e.g., system prompts and document context), costs can be reduced by 90% or more.

What is the difference between CAG (Cache Augmented Generation) and RAG?

CAG (Cache Augmented Generation) pre-caches knowledge base documents directly into the prompt, so each request can use them without retrieval. RAG (Retrieval-Augmented Generation) requires retrieval of relevant documents for every request. CAG is suitable for scenarios with a smaller document set but frequent access, providing faster responses; RAG is better for large-scale knowledge bases—more flexible, but with higher latency.

Why is response caching not suitable at high temperatures?

High temperature (e.g., temperature > 0.7) makes the LLM output more random and diverse. The same prompt may produce completely different responses. Caching such responses loses the value of diversity and may also lead users to receive stale or repetitive answers. Therefore, response caching is more suitable for low-temperature or deterministic scenarios.