Embedding Quality Evaluation — Benchmark for Your Task

I will conduct a comprehensive embedding quality evaluation for your use case — running multiple embedding models against your domain-specific retrieval benchmark, measuring Recall@k, MRR, NDCG, and semantic similarity on your test queries, and delivering a model comparison report with a data-backed recommendation. MTEB benchmark scores are useful for general model comparison but are poor predictors of domain-specific performance — a model that ranks highly on MTEB may rank poorly on your specialised domain retrieval task; only evaluation on your actual data and queries provides reliable guidance for model selection.

The evaluation covers benchmark design using your domain data, test query set curation with relevance annotations, 3-5 embedding model comparison, Recall@k, MRR, and NDCG metric calculation, latency and cost comparison, and a recommendation report with evidence.