Lilly Tech Systems
Prompt Caching
Slash your AI API costs and latency by caching repeated prompt content. Learn how Anthropic, OpenAI, and other providers implement prompt caching, and how to design your applications to maximize cache hit rates.
What You'll Learn
By the end of this course, you'll know how to implement prompt caching across all major AI providers and optimize your applications for maximum cost savings.
Cost Reduction
Understand how prompt caching can reduce input token costs by up to 90% for repeated content across API calls.
Latency Optimization
Learn how cached prompts skip re-processing, reducing time-to-first-token and improving user experience.
Provider Strategies
Compare caching mechanisms across Anthropic (explicit) and OpenAI (automatic), with implementation details for each.
Architecture Patterns
Design your prompt structure to maximize cache hits — from system prompts to multi-turn conversations.
Course Lessons
Follow the lessons in order for a complete understanding, or jump to any topic.
1. Introduction
What is prompt caching? How it works at a high level, why it matters for production AI, and the key concepts you need to know.
2. Anthropic Caching
Anthropic's explicit cache control with cache_control breakpoints. How to mark cacheable content and monitor cache performance.
3. OpenAI Caching
OpenAI's automatic prompt caching for GPT-4o and newer models. How prefix matching works and what gets cached automatically.
4. Cost Savings
Calculate real-world cost savings from prompt caching. Pricing comparisons, ROI analysis, and break-even calculations for different usage patterns.
5. Implementation
Production implementation patterns: prompt structuring, cache warming, monitoring cache hits, handling cache misses, and multi-turn optimization.
6. Best Practices
Production checklist for prompt caching: ordering strategies, TTL management, debugging, monitoring dashboards, and common mistakes.
Prerequisites
What you need before starting this course.
- Basic understanding of AI API usage (sending prompts, receiving responses)
- Familiarity with token concepts (see our Tokens course)
- Experience with Python or JavaScript for code examples