Style guides, tone-of-voice guidance, locale conventions, and review guidelines define what “good” looks like for a specific brand and use case. They turn subjective preferences into shared standards, so linguists and reviewers make consistent decisions instead of improvising.
Termbases and glossaries capture approved terms, definitions, context, and do-not-use rules. They protect precision in regulated content, stabilize naming in products and UI, and prevent the slow drift that creates inconsistent messaging across markets.
Translation memories and approved phrase libraries reduce repetition and keep phrasing consistent at scale. The value comes from hygiene and governance: separating trusted from legacy content, preventing duplication, and updating assets as terminology and style evolve.
Context packs, screenshots, approved examples, and comparable localized materials reduce ambiguity. They help linguists match product reality and brand intent — especially in UI, marketing, and content that depends on tone and audience expectations.
Checklists, automated QA rules, and LQA profiles (with content-type weighting modes) keep quality control consistent and efficient. They focus attention on what creates real downstream cost: meaning errors, terminology drift, formatting issues, and constraint violations.
Decision logs, ownership rules, and escalation paths define who can approve terminology and style changes — and how exceptions are handled. This keeps assets current, avoids contradictory feedback, and makes improvements cumulative across projects and stakeholders.
If AI is used anywhere in the workflow, prompt templates, constraints, and post-edit rules must be treated as assets. Managed properly, they reduce variance and risk; unmanaged, they amplify inconsistency. We treat AI guidance as a governed layer, not ad hoc improvisation.
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