Retrieval-Augmented Generation (RAG) is everywhere. So is AI memory. They both involve semantic search and injecting retrieved content into LLM prompts. So what's the difference — and when do you use each?
RAG: Retrieving From Static Knowledge
RAG is designed for retrieval from a fixed, shared knowledge base. Your product documentation, a legal corpus, a company knowledge base — content that doesn't change per-user and doesn't accumulate over time.
In RAG, you're answering: "What does my knowledge base know about this topic?"
Memory Retrieval: Retrieving From Dynamic User History
Memory retrieval is designed for per-user, dynamic, accumulating data. Every user has their own memory store that grows with every interaction. The retrieved content is personal, specific, and time-sensitive.
In memory retrieval, you're answering: "What does this specific user know, prefer, or have experienced?"
The Key Differences
| Dimension | RAG | Memory Retrieval |
|---|---|---|
| Data source | Static documents | Dynamic user history |
| Scope | Shared across all users | Per-user isolated |
| Content | Factual knowledge | Personal context & preferences |
| Growth | Controlled by you | Grows with each interaction |
Most Great AI Apps Use Both
A customer support bot might use RAG to retrieve from the product documentation ("How do I reset my password?") and memory retrieval to provide personalized context ("I see you've contacted us about this before — here's what we tried last time").
The architectures are complementary. RAG makes your AI knowledgeable. Memory makes it personal.