Vector embeddings excel at semantic understanding but fail on exact matches, negations, and company-specific identifiers. A new analysis reveals how RAG systems initially impress by handling paraphrases and typos but later frustrate users when they can't locate specific contract numbers or technical terms. The study tests four embedding models showing improved performance but fundamental limitations remain in enterprise document retrieval.

Testing models from GloVe-avg (2014) to OpenAI's latest embeddings shows better semantic proximity but doesn't solve core issues. Enterprise reliability gains come from strong upstream filtering rather than stacking rerankers on weak retrieval. The position stated upfront: embedding similarity alone cannot overcome fundamental retrieval failures for exact specifications like contract numbers or internal codes.

The analysis documents specific failure cases where embedding search failed against exact keyword matching. The article concludes that document intelligence requires combining semantic understanding with precise term recognition. This research provides practical guidance for building enterprise RAG systems that balance flexibility with accuracy without relying solely on embedding magic.