This is why every approach is fundamentally wrong.

Correctly pointed out the limitations of RAG: bias, outdated information, and illusion issues do affect its reliability. Compared to pure generative models, RAG is stronger in factualness and traceability; compared to knowledge graphs, RAG is more flexible; compared to fine-tuning models, RAG is cost-effective and widely adaptable. Its core strengths lie in dynamic updates, traceability, and domain adaptability, suitable for scenarios that require rapid access to factual evidence. However, to fully unleash its potential, improvements are needed in knowledge base quality, retrieval accuracy, and generation constraints. Users should be aware that RAG outputs are not entirely "real," but based on approximate retrieval content.

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