Dev Tools · 1h ago
Why enterprise search fails and how embeddings fix it
A CEO's three-day search for a contract clause cost $40,000 because keyword search missed synonyms like 'limitation of liability' for 'liability cap'. Embedding models convert text into vectors that measure semantic similarity, matching meaning instead of exact words. This approach can retrieve relevant documents even when phrasing differs completely.
Meridian48 take
The article correctly diagnoses a common enterprise pain point, but deploying embeddings at scale requires careful tuning and infrastructure investment beyond the simple Python snippet shown.
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