I'm exploring ways to boost the factual reliability of large language models without sacrificing fluency. Retrieval-augmented generation (RAG) inserts external knowledge at inference time, but I'm unsure how robust it is across diverse domains and ambiguous queries. Does RAG generally lead to measurable accuracy gains, or are there hidden pitfalls like latency or source credibility issues? I'd appreciate any insights on best practices, evaluation methods, or alternative approaches you’ve seen work.
Can retrieval-augmented generation reliably improve factual accuracy of LLMs?
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I’ve found RAG often outperforms plain prompting on factual recall, but a curated knowledge‑graph or filtered vector‑store lookup can be more reliable when source credibility matters, since RAG may still surface low‑quality passages and add latency.