text-embedding-ada-002 has several limitations that developers should be aware of when using it in production. While it provides solid general-purpose embeddings, it may not capture very fine-grained semantic distinctions as well as newer models. For applications that demand the highest possible retrieval precision, this can affect ranking quality.
Another limitation is that embeddings are static once generated. If the underlying text changes, the embedding must be regenerated to stay accurate. This means developers need to plan for re-embedding workflows when documents are updated. Additionally, embedding quality depends heavily on preprocessing choices such as chunk size and text normalization. Poor preprocessing can reduce effectiveness regardless of model quality.
From an infrastructure standpoint, text-embedding-ada-002 produces 1536-dimensional vectors, which have storage and memory costs at scale. Using a vector database such as Milvus or Zilliz Cloud helps manage these costs efficiently, but developers still need to plan capacity and indexing strategies carefully. Understanding these limitations upfront helps avoid surprises in production systems. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002