Why Architects Should Care About RAG

Retrieval-Augmented Generation isn't just an AI technique — it's a systems architecture pattern that every architect should understand.

2 min read
RAGArchitectureAIOpinion

The Pattern Behind the Hype

RAG (Retrieval-Augmented Generation) has become one of the most talked-about patterns in AI. But strip away the ML jargon, and what you find is a familiar architectural pattern: query, enrich, respond.

As architects, we’ve been building systems like this for decades. A request comes in, we fetch relevant context from a data store, combine it with the request, and generate a response. RAG applies this exact pattern to language models.

Why This Matters

The implications for system design are significant:

  1. Data freshness — LLMs are trained on static data. RAG lets you augment with real-time information.
  2. Domain specificity — Instead of fine-tuning (expensive, slow), you retrieve domain knowledge at query time.
  3. Observability — You can trace which documents influenced a response, unlike pure model inference.
  4. Cost — Retrieval is cheaper than training larger models.

The Architecture

At a high level:

  1. User query comes in
  2. Query is embedded into a vector
  3. Vector similarity search against a knowledge base
  4. Top-K relevant documents are retrieved
  5. Documents + query are sent to the LLM as context
  6. LLM generates a grounded response

This is just a read-through cache pattern with vector similarity as the lookup mechanism.

Takeaway

If you understand caching, pub/sub, and query optimization, you already understand 80% of RAG. The remaining 20% is embedding models and prompt engineering — learnable in a weekend.

Don’t let the AI branding scare you off. This is systems architecture.