Why semantic search similarity alone fails to deliver truly relevant results

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A software engineer building the GovernAI Research Atlas, a semantic research discovery platform, found that vector-based similarity search alone often returns technically related but practically unhelpful results. The platform indexes papers, code repositories, and policy documents from sources like OpenAlex and GitHub using ChromaDB and Sentence-Transformer embeddings. To address the gap between similarity and relevance, the developer added a custom scoring layer on top of standard nearest-neighbor retrieval to reorder candidates by actual usefulness. This two-stage approach — broad semantic retrieval followed by deliberate ranking — is described as the key architectural pattern separating a functional search product from a basic demo. The project highlights that the most critical engineering challenge in search lies not in retrieval, but in determining which result deserves the top position.

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