text-embedding-3-small’s main limitations in production are that embeddings are an approximate semantic signal, quality depends on data preparation, and retrieval pipelines still require careful evaluation and monitoring. Even strong embedding models can fail on edge cases: very short queries (“crash”), highly domain-specific jargon, ambiguous terms, or queries that require precise numeric/logical matching. Embeddings are great at “meaning similarity,” but they are not a substitute for structured filters, exact matching for IDs/version numbers, or domain rules.
In practice, you’ll notice limitations in a few recurring patterns. First, “semantic drift” can happen when a chunk contains multiple topics; the embedding may match a query for the wrong reason. Second, embeddings don’t inherently enforce freshness, permissions, or compliance boundaries—those need to be handled in metadata and query filters. Third, multilingual and code-heavy content can be uneven depending on your domain; for example, stack traces, config snippets, and code symbols might require additional preprocessing (split by delimiters, keep key tokens, or store a separate field for exact keyword search). Finally, updates are operationally real: if your content changes often, you need a strategy for re-embedding and re-indexing without serving stale or inconsistent results.
These limitations become manageable when you pair text-embedding-3-small with a vector database such as Milvus or Zilliz Cloud and design the pipeline as a system, not just a model call. Use chunking to reduce multi-topic embeddings, store rich metadata for filters (tenant, ACL, doc version, language), and build an evaluation set that reflects real user queries. Monitor metrics like “no result rate,” “top-1 click rate,” and “time to first useful result,” and keep an eye on drift after content or product changes. In production, the most successful teams treat embeddings as one strong signal in a retrieval stack and invest in the boring parts: preprocessing, indexing, filtering, and regression testing.
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-small