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[[{“value”:”Thanks to Descope for sponsoring this video, checkout Agent Identify Hub: https://descope.plug.dev/BWwF1nd
I break down why AI model prices are rising at most labs while DeepSeek cut V4 Pro pricing by 75%, and why prompt caching is the key. I explain the two phases of an LLM request (compute-bound prefill vs memory-bound decode), what the KV cache stores, and why reusing cached prefixes can cut cost and latency, citing the “Don’t Break the Cache” paper’s reported savings. I then cover how DeepSeek’s multi-head latent attention (MLA) shrinks KV cache enough to store it on a distributed disk array instead of expensive HBM, enabling cheap cache-hit pricing. Finally, I share Anthropic/Claude Code’s cache-preserving request structure and the main cache-busters (model/tool changes, dynamic system prompts, naive compaction, upgrades), plus cache-friendly patterns like plan mode tools, cache-safe compaction, and using /rewind.
00:00 AI Price Wars
01:11 Prompt Caching Explained
02:29 What KV Cache Stores
03:53 DeepSeek Disk Caching
05:55 Sponsor Agent Identity
07:48 Claude Code Cache Layers
08:42 Five Cache Busters
11:22 Messages Not Prompts
12:17 Cache Friendly Features
My voice to text App: whryte.com
Website: https://engineerprompt.ai/
RAG Beyond Basics Course:
https://prompt-s-site.thinkific.com/courses/rag
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