"Most (if not all) embedding-based retrieval use approximate nearest neighbours (ANN). If we use exact nearest neighbours, we would get perfect recall of 1.0 bu

"Most (if not all) embedding-based retrieval use approximate nearest neighbours (ANN). If we use exact nearest neighbours, we would get perfect recall of 1.0 but with higher latency (think seconds). In contrast, ANN offers good-enough recall (~0.95) with millisecond latency. I’ve previously compared several open-source ANNs and most achieved ~0.95 recall at throughput of hundreds to thousands of queries per second." Can the results of multiple very fast, approximate queries somehow be used to get the equivalent of one very slow, reliable query?