What is RAG

Why RAG exists — the gap between static model knowledge and live retrieval, and when RAG is the right answer.

What You Will Learn

• The knowledge cutoff problem: why Claude does not know your documents
• RAG concept: retrieve relevant content, add to context, generate answer
• When RAG is the right choice vs alternatives (fine-tuning, long context)
• Simple RAG in one diagram: query → retrieve → augment → generate
• What RAG cannot do: it retrieves, Claude generates — errors can come from both