How Japan’s Research Labs Are Building RAG Systems That Actually Work — And What Western Teams Keep Getting Wrong (opens in new tab)

Your vector database is returning relevant chunks. Your embedding model scores 0.89 on retrieval benchmarks. Your PM calls it "AI-powered search." But when a researcher asks "what are the methodological limitations of study X given our lab's prior work?", the system returns a paragraph about the weather in Tokyo. This is the retrieval hallucination problem — and it's not a model failure. It's a retrieval architecture failure that no amount of LLM tuning fixes. I found an approach that actuall...