Editing Embeddings Vector Dbs (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert practitioners evaluate embedding systems at multiple levels:

'''Embedding quality metrics''':
* '''MTEB (Massive Text Embedding Benchmark)''': The standard benchmark suite for text embeddings, covering retrieval, classification, clustering, semantic similarity, and more.
* '''BEIR benchmark''': Zero-shot retrieval across diverse domains — the true test of embedding generalization.

'''System-level retrieval metrics''':
* Recall@k and Precision@k on held-out query-document pairs
* Mean Reciprocal Rank (MRR) for ranking quality
* Query latency at p50/p95/p99 percentiles

'''Operational metrics''':
* Index build time and storage size (cost)
* Query throughput (QPS) at target latency SLAs
* Index freshness lag (time between document update and searchability)

Expert practitioners also evaluate embeddings on their specific domain — a general embedding model trained on web text may underperform a fine-tuned domain-specific model on medical, legal, or code retrieval tasks.
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