Vector similarity searches return no results, wrong/irrelevant documents, or "I don't know" despite matching content exists. Exact keyword search works; other vector stores fine. Mimics index corruption but queries succeed without errors.

pgvector approximate indexes (IVFFlat/HNSW) are trained on data distribution for clustering. Indexing too early (<sufficient data), wrong params (low lists/probes), or after major data shifts creates suboptimal clusters, causing poor recall/wrong results (not true corruption). Queries trade perfect recall for speed; results change post-indexing per design.