Lesson 1006 of 1596
Prompt Caching Comparison: Anthropic, OpenAI, Gemini
How prompt caching works across vendors and where it pays off.
Creators · Model Families · ~24 min read
The premise
Prompt caching cuts cost dramatically when cache-hit rates are high — vendor implementations differ in critical ways.
What AI does well here
- Cache long system prompts and tool schemas (all vendors).
- Cut token cost 50-90% on cache hits.
- Reduce latency on cached prefix reads.
What AI cannot do
- Match cache implementations across vendors exactly.
- Cache user-specific or rapidly-changing context effectively.
Key terms in this lesson
Practice this safely
Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.
- 1Ask AI to explain cache hit rate in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "Prompt Caching Comparison: Anthropic, OpenAI, Gemini" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check prompt caching against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
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