Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval

Retrieval-Augmented Generation (RAG) systems initially impress by handling paraphrases and synonyms, but they predictably fail in enterprise contexts. These failures occur with specific queries involving negation, exact identifiers, or internal company acronyms, which vector search alone cannot reliably handle. The content posits that robust enterprise solutions depend more on strong upstream filtering with keywords and document structure than on simply improving weak retrieval with rerankers. It then demonstrates the strengths of modern embeddings, comparing models from GloVe to OpenAI's text-embedding-3-large to show improvements in handling synonyms, typos, and cross-lingual queries.