v202 Oct 2026abhydesai5 min 57 sec
Re-rendered in the studio with chapters and a transcript.
v1 has no transcript to compare. 8 sec shorter.
0 views0 likes on this version
A visual explainer of prompt caching for teams building on language models. Covers why identical content can cost 10x more depending on token order, how the KV cache stores a prompt's stable prefix instead of recomputing it, the prefix tree that Anthropic, OpenAI, and Gemini use to serve cache hits, what happens on eviction, and the design pattern of putting stable content first for lower cost and faster time to first token.
Re-rendered in the studio with chapters and a transcript.
v1 has no transcript to compare. 8 sec shorter.
First published cut.