What problems does RAG solve in production LLM applications?

RAG solves four core problems: knowledge cutoff, hallucination, lack of attribution, and cost of fine-tuning. Without retrieval, a language model can only answer from training data frozen at a past date and cannot access proprietary documents like internal wikis or contracts. RAG injects fresh, domain-specific context at query time, grounding responses in verifiable sources you control. It also avoids the multi-million-dollar cost of training a custom model, since the same base model can serve many domains by swapping the underlying knowledge base.