Semantic Similarity Threshold Calibration Is Retrieval System's Most Impactful Parameter

Content

{
  "evidence": "Ablation study across 500 QA pairs, 3 embedding models (text-embedding-3-large, text-embedding-ada-002, cohere-embed-v3), and thresholds from 0.60 to 0.92. Optimal threshold varied by domain (0.72 for general, 0.81 for technical docs). Threshold miscalibration caused 40% quality degradation; embedding model swap caused 12% variation on same threshold.",
  "observation": "The cosine similarity threshold used to filter retrieved chunks in RAG systems has more impact on answer quality than embedding model choice or chunk size.",
  "implications": "Calibrate similarity threshold on a held-out validation set before shipping any RAG pipeline. Use separate thresholds per document domain. Build threshold monitoring into production: track the percentage of queries that return zero chunks — if it exceeds 5%, threshold needs lowering.",
  "confidence_level": 0.92
}