RAG Architectures AI Builders Should Understand

Retrieval-augmented generation (RAG) addresses a critical gap in AI systems by creating an evidence-based pathway that keeps responses current, respects privacy boundaries, and provides verifiable sources rather than relying on potentially outdated training data. RAG works by first retrieving relevant evidence from databases or documents, then constraining the AI's response to cite only that retrieved information, creating an auditable trail from question to answer. The architecture ranges from basic RAG for simple Q&A to complex agent-driven systems for multi-step analysis, with each trading off latency, cost, and accuracy based on whether mistakes are harmless or expensive.