A fuzzy match is a translation memory result that shares some, but not all, of its source text with the current segment, typically expressed as a percentage below 100%. In CAT tool workflows, fuzzy matches are offered to translators as editable suggestions so they can reuse previously translated wording instead of starting from scratch, and they are priced differently from exact matches.
Fuzzy matches appear in CAT tool panes, translation memory reports, and pricing grids that break down match types such as 75–99%, internal repetitions, and exact matches. Project managers assign discount rates or thresholds for fuzzy matches, and linguists check whether the suggestion fits the current source segment context before accepting it.
Treating a high fuzzy match as an exact match can inject outdated or contextually wrong wording, especially when numbers, negation, or product names differ from the memory entry. Relying too heavily on fuzzy suggestions also causes inconsistent style if translators accept them blindly without reviewing the target sentence as a whole.
For creative and consumer content the failure mode is flatness, not inaccuracy — and flatness passes an accuracy check. Terms here also carry technical constraints (reading speed, character budgets) that are part of the brief.
Angel Translation locks terms like this into a project terminology base before expert review begins, so the same source term resolves to the same target term across every document in a submission — and stays consistent in the next revision. See the 8-step AI + expert workflow.
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