Gemini Context Caching on Vertex AI (ANSWERED)
Medium Gemini/Vertex question on context caching vs RAG trade-offs.
TL;DR — Quick Answer
Context caching stores large prompt prefixes for reuse across requests, cutting cost/latency when many queries share the same documents. Prefer caching for hot, moderately sized corpora reused often; prefer RAG when the corpus is huge, sparsely accessed, or needs ACL-filtered retrieval.
The Interview Question
How does context caching work with Gemini on Vertex AI, and when would you cache a large document corpus versus using RAG?
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