A hands-on test of BGE-M3 + Qwen3 (RAG vs. direct-context answering) on a real research paper and a full-length book including a retrieval bug hiding in a footnote, and one surprisingly good model behavior.
I wanted to answer a simple question: when you feed a document to an AI model, is it actually reading it or just pattern-matching to whatever text happens to look similar to your question?
So I built a small open-source pipeline to test this directly. For any document and question, it generates two separate answers:
RAG answer: BGE-M3 finds the most relevant chunks of the document, and Qwen3 answers using only those chunks.
Direct answer: Qwen3 reads the raw document text directly, no retrieval involved.
