Does this skill work specifically with Anthropic's Claude?

Yes, it includes specific patterns for Anthropic's native prompt caching feature, which allows for the caching of specific prefixes in long conversations.

What is the primary benefit of using this prompt caching skill?

The primary benefit is a significant reduction in API costs (up to 90%) and a major decrease in response latency by reusing previously processed context.

How does Cache Augmented Generation (CAG) differ from RAG?

CAG pre-caches static documents directly in the prompt context rather than performing real-time retrieval, making it faster and more cost-efficient for frequently accessed data.

When should I avoid caching LLM responses?

You should avoid caching when using high-temperature settings where variability is required, or for highly dynamic data where cached responses would quickly become stale.

LLM Prompt Caching & Optimization

Name: LLM Prompt Caching & Optimization
Author: claudiodearaujo

byclaudiodearaujo

0•

Data Science & ML

Reduces LLM latency and API costs by implementing advanced prefix, response, and Cache Augmented Generation (CAG) strategies.

This skill acts as a high-performance caching specialist designed to optimize LLM interactions and slash operational costs by up to 90%. It provides expert guidance on implementing multi-level caching architectures, including Anthropic's native prompt prefix caching, full response caching, and semantic similarity matching. By leveraging Cache Augmented Generation (CAG) patterns to pre-cache documents, it helps developers bypass expensive RAG retrievals while maintaining high accuracy and performance in AI-driven applications.

Key Features

01Implementation of Anthropic's native prompt prefix caching for long contexts

02Sophisticated cache invalidation and KV-cache management logic

03Prompt restructuring techniques to maximize cache hit rates

040 GitHub stars

05Multi-level response caching for identical or semantically similar queries

06Cache Augmented Generation (CAG) patterns to optimize document retrieval

Use Cases

01Reducing API billing for high-traffic AI applications and agents

02Optimizing large-scale document processing without frequent RAG overhead

03Lowering response latency for repetitive user queries and workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro-front prompt-caching

For use in Claude.ai and ChatGPT

Download Skill