RAG vs CAG: Understanding When to Use Each
There has been much discussion about whether CAG (Cache Augmented Generation) is replacing RAG (Retrieval Augmented Generation). The answer, as it often turns out, is: "It depends."
Quick Definitions
RAG (Retrieval Augmented Generation) fetches relevant chunks from an external knowledge base on every query, then conditions the language model on them. Think of it as a librarian who searches through the stacks each time you ask a question, pulling the most relevant books to help answer your query.
CAG (Cache Augmented Generation) preloads a large static corpus into a long-context LLM once, builds a KV cache of that context, and reuses it across many queries. This avoids repeated retrieval and recomputation. Imagine having all the relevant books already open on your desk, ready to reference instantly.
The Real Story: Complementary, Not Competitive
Despite the buzz suggesting CAG might replace RAG, the reality is more nuanced. These approaches address different challenges and often work best together. Here's when each shines:
Choose RAG when you need:
Dynamic, frequently changing knowledge (pricing, inventory, policies, real-time analytics)
Access to massive corpora that exceed practical context window limits
Vendor neutrality and the flexibility to swap LLM providers
Clear attribution trails for compliance and debugging
User-specific or multi-tenant data that can't be preloaded
Choose CAG when you have:
Relatively static knowledge bases that fit within context limits
High-query-volume workloads over the same corpus
Latency-sensitive applications where every millisecond counts
Simpler architectural requirements without separate retrieval infrastructure
FAQ systems, support playbooks, or internal documentation with stable content
The Hybrid Future
The most sophisticated systems are already combining both approaches. You might use CAG for your "always-on" background knowledge—product catalogs, base documentation, core policies—while layering RAG on top for fresh, dynamic, or user-specific data. This "RAG over CAG" pattern gives you the speed of caching with the flexibility of retrieval.
As context windows continue expanding and serving infrastructure improves, the design space will keep evolving. But one thing is clear: RAG isn't going anywhere. It's adapting, becoming more sophisticated with multi-modal retrieval, graph-aware search, and tighter integration with operational systems where CAG simply doesn't fit.
Bottom Line
If your knowledge is small, stable, and shared across users, bias toward CAG-first with minimal retrieval. If your knowledge is large, dynamic, or personalized, treat CAG as an optimization layer around a fundamentally RAG-like architecture, not a replacement for it.
Ready to dive deeper? Check out the audio discussion that explores these concepts in more detail.