Q. Retrieval quality is poor even though the embeddings look fine. What silent config is worth checking?
A. The index distance metric must match how the embedding model was trained (cosine, Euclidean/L2, or dot product). A mismatch quietly wrecks ranking, and the vector dimensions must match the model too.
Why? Dimension and distance-metric mismatches produce plausible-but-wrong retrieval, a classic trap.