How Japan’s Research Labs Are Building RAG Systems That Actually Work — And What Western Teams Keep Getting Wrong

Short summary

Japanese research labs achieved 90% accuracy on scientific Q&A using knowledge-graph RAG that explicitly models entity relationships instead of pure semantic similarity. GraphRAG requires 2-3x more infrastructure and ongoing maintenance but prevents retrieval hallucinations in high-stakes domains like research and legal. Trade-off: slower build time and higher maintenance versus better accuracy on relationship questions.

• •Knowledge graph RAG achieves 90% accuracy by modeling explicit entity relationships, not just semantic similarity
• •Requires 2-3x infrastructure and ongoing graph maintenance compared to standard RAG
• •Recommended for high-stakes domains (research, legal); standard RAG sufficient for general Q&A

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