Prompt Caching Explained: Why Prefixes Matter

In this video, we walk through how prompt caching actually works inside major LLM APIs and show why two prompts with identical content can end up with wildly different bills. By the end, you'll know exactly why prompt order matters and how to structure prompts so the cache works for you on every call. You'll learn how to: Understand what KV vectors are and why every token position triggers fresh computation See how prefix trees structure cached attention state across requests Identify why cache hits depend on prefix order, not total content Structure prompts so stable content sits first and variable content sits last Recognize the silent failure mode of cache eviction Apply this design pattern across Anthropic, OpenAI, and Gemini APIs Timestamps: 0:00 - The mystery of two identical prompts with very different bills 0:33 - What the model actually computes on every call 1:20 - The key insight: stable head, variable tail 2:13 - How the prefix tree builds up request by request 3:26 - Why prompt order decides whether you get a cache hit 4:19 - The silent problem of cache eviction 4:59 - The prompt structure pattern to follow 5:45 - Cached token discounts across major providers This video is for AI engineers, backend developers, and technical founders who want to lower their LLM API costs and improve time to first token without changing models or trimming content. Clyep produces technical videos for complex software products, including product demos, developer tutorials, release videos, and technical explainers. Learn more: https://clyep.io/ If you found this useful, subscribe for more technical walkthroughs and explainers.