@akshay_pachaar: RAG vs. CAG, clearly explained! RAG is great, but it has a major problem: Every query hits the vector DB. Even for stat…
Summary
Explains Cache-Augmented Generation (CAG) as a method to cache static knowledge directly in the model's KV memory, reducing latency and cost compared to traditional RAG, and shows how to combine both for optimal performance.
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RAG vs. CAG, clearly explained!
RAG is great, but it has a major problem:
Every query hits the vector DB. 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.
In fact, 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
This gives faster inference, lower costs, and 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 my recent article on prompt caching below if you want to dive deeper.
Have you tried CAG in production yet?
Below, I have quoted an article that I wrote on prompt cashing and how Claude Code achieves a 92% cache hit-rate. Give it a read.
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