You evaluate results from text-embedding-3-large by measuring retrieval quality against real queries and expected outcomes, not by inspecting embeddings directly. Since embeddings are numerical representations, their quality is best judged through downstream behavior such as search relevance or recommendation accuracy.
A common evaluation approach is to create a test set of queries with known relevant documents or items. You then measure metrics such as recall@K, precision@K, or mean reciprocal rank (MRR). For recommendation systems, click-through rate or user engagement metrics are often used. Qualitative review is also important: developers or domain experts should manually inspect top results for representative queries to catch obvious mismatches or missing results.
Vector databases like Milvus and Zilliz Cloud make evaluation easier by allowing fast experimentation with different index settings, similarity metrics, and chunking strategies. You can maintain multiple collections to compare configurations side by side. Over time, it’s important to rerun evaluations as content changes or new data is added. Consistent evaluation practices help ensure that text-embedding-3-large continues to meet quality expectations in production.
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