AI Context Caching Tutorial: Cutting Cost

TL;DRContext (prompt) caching reuses repeated prompt prefixes to cut cost and latency.

If many requests share a long prefix (a big system prompt, a document), recomputing it every time is wasteful. Prompt/context caching stores the processed prefix so repeated calls skip that work — major cost and latency savings for chatbots and RAG with stable instructions. Structure prompts with the stable, cacheable part first and the variable part last to maximize cache hits.

Key points

Common mistakes

Try it: Explain how prompt structure affects cache hit rate.

Example code

<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>[ stable system prompt + docs ] ← cache this
[ variable user question ]      ← changes
stable-first → high cache hits → cheaper/faster</pre></body></html>
Open the interactive lesson →
Synthetic Data · Self-Instruct · Distill Structured Outputs · JSON Mode · Constrained