A tricky LLM interview question: Your RAG system scores 90% retrieval accuracy on 5k company docs. But scaling to 500k docs drops the accuracy to just 50%, with the same embedding model and retriever. Why did this happen? The simplest answer is that more documents mean more competition for the top-k retrieval slots. That is true, but it doesn't explain why accuracy drops this dramatically. The answer comes down to how enterprise docs are distributed in the embedding space. Today, a single product decision in a company generates meeting transcripts, Slack threads, Confluence docs, Jira tickets, and email threads. They are related to the same event, so they all land in a similar region of the embedding space. As the company operates over months, this pattern repeats for every project/customer/roadmap, and the embedding space fills up with clusters of closely related documents. But all related docs don't contain the same facts. → Slack thread covers the decision made → Jira has the implementation deadline → Confluence has the technical spec → Email thread has the customer request When a query is about a specific fact (like a deadline), the answer lives in one of those docs. At a 5K corpus size, there might be 3-5 docs touching that topic, and the correct one easily lands in the top-k results. But at a 500K corpus size, there could be 40-60 total docs, and the one containing the actual answer can easily get pushed out of the top-k by other topically relevant docs, degrading retrieval. A recent research paper from Onyx documented this. The researchers used their newly open-sourced EnterpriseRAG-Bench dataset. It has 500k+ synthetic enterprise documents spread across Slack, Gmail, Jira, GitHub, Confluence, Google Drive, HubSpot, Fireflies, and Linear, with realistic noise like misfiled documents, near-duplicates, and conflicting versions. They ran the same retrievers at five corpus sizes from 5K to 500K. → Vector search accuracy dropped from 90.7% at 5K documents to 50.6% at 500K docs. → BM25 degraded more gracefully, from 85.8% to 68.4%. → At every scale, higher neighborhood density in the embedding space monotonically correlated with lower recall. The practical implication here is that retrieval accuracy on a 5k test set tells you almost nothing about production-scale performance. Always test at a realistic volume to measure the neighborhood density in your embedding space to estimate how much headroom the retriever actually has. The entire EnterpriseRAG-Bench dataset (500K docs with questions, and the whole evaluation harness) is open-source. Run your retriever against it at 5K, then at 500K, and see where your own accuracy curve breaks. I have shared the GitHub repo in the replies.