text-embedding-ada-002 is generally accurate for a wide range of common semantic tasks, including search, clustering, and basic classification. It provides consistent and reliable embeddings that capture the main meaning of text rather than surface-level word overlap. For many applications, this level of accuracy is sufficient to deliver good user experiences without complex tuning.
In real-world usage, accuracy often depends on how the embeddings are applied. For example, breaking long documents into well-sized chunks before embedding usually improves retrieval quality. Similarly, choosing an appropriate similarity metric and indexing strategy can have a noticeable impact on results. While newer models may offer higher semantic precision, text-embedding-ada-002 remains dependable for general-purpose workloads.
Accuracy also depends on the surrounding infrastructure. When embeddings are stored and searched using a vector database such as Milvus or Zilliz Cloud, developers can take advantage of efficient indexing and filtering to improve result quality. Proper indexing parameters and query tuning often matter as much as the embedding model itself. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002