Despite its advantages, RAG systems face challenges such as dependency on the quality and relevance of external data sources. If retrieval fails or sources are outdated, AI can still generate errors. Moreover, implementing RAG adds complexity and computational cost, requiring efficient indexing, retrieval, and integration strategies. Privacy concerns also arise when sensitive or proprietary data is involved in retrieval processes.