From Keyword Buckets to Meaning Clusters
Explain why embeddings group text by meaning rather than exact words alone.
Same customer feedback, two search approaches. What changed? Before: exact-word folders After: semantic clusters The unit changed from exact word match to vector similarity, so related phrases can group together even when vocabulary differs. The analyst still reviews the clusters, because embeddings suggest semantic closeness rather than final business judgment. Retrieved clusters become useful LLM context only when the relevant examples are inserted into the generation prompt. Vector closeness is a search signal, not a final answer. Embedding intuition You moved from looking for matching words to looking for nearby meaning. Ask what idea the phrase represents, not just which…
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