RAG vs. CAG, clearly explained!
RAG is great, but it has a major problem:
Every query hits the vector database. Even for static information that hasn't changed in months.
This is expensive, slow, and unnecessary.
Cache-Augmented Generation (CAG) addresses this issue by enabling the model to "remember" static information directly in its key-value (KV) memory.
Even better? You can combine RAG and CAG for the best of both worlds.
Here's how it works:
RAG + CAG splits your knowledge into two layers:
↳ Static data (policies, documentation) gets cached once in the model's KV memory
↳ Dynamic data (recent updates, live documents) gets fetched via retrieval
The result? Faster inference, lower costs, less redundancy.
The trick is being selective about what you cache.
Only cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating "cold" (cacheable) and "hot" (retrievable) data keeps this system reliable.
You can start today. OpenAI and Anthropic already support prompt caching in their APIs.
I have shared a link to OpenAI's prompt caching guide in the replies.
Have you tried CAG in production yet?
OpenAI prompt caching guide: https://platform.openai.com/docs/guides/prompt-caching
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