Vector Embedding Similarity Search Language Collocations
Practise the standard verbs for building and validating embedding-based similarity search.
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Fill in: 'We ___ an embedding for every document at ingest time so a similarity search can compare meaning, not just overlapping keywords.'
We 'generate an embedding' — the standard, established collocation for producing a vector representation of a document. The other options aren't the recognised term here.
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Fill in: 'Skipping vector normalization before indexing can ___ cosine similarity scores that are meaningless across differently scaled documents.'
We say skipped normalization will 'leave' scores incomparable — the standard, natural collocation for the resulting problem. The other options aren't idiomatic here.
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Fill in: 'We ___ an approximate nearest-neighbour index over the embedding store so a similarity search returns results in milliseconds, not seconds.'
We 'build an index' — the standard, simple collocation for constructing a search structure over stored vectors. The other options are less idiomatic here.
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Fill in: 'We ___ recall against a brute-force baseline before trusting an approximate index in production, since it may miss true nearest neighbours.'
We 'check' recall — the standard, simple collocation for validating a metric against a known-correct baseline. The other options are less idiomatic here.
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Fill in: 'We ___ query latency for the nearest-neighbour search continuously, since a growing index can quietly push retrieval past its budget.'
We 'monitor' latency — the standard collocation for ongoing observation of a metric over time. The other options aren't idiomatic here.