Machine translation has matured in 2026 — output quality rivals mid-level human translation on most content. Human post-editing still essential for high-stakes or culturally-sensitive work, but the workflow has shifted: AI drafts, humans refine. This post is the specific translation workflows that work at scale and the quality/cost tradeoffs between approaches.
Source preparation
Terminology extraction. Identify key terms; establish target-language equivalents; ensure consistency across documents.
Style guide. Target tone, formality level, target audience, brand voice. Guides both MT output and human post-edit.
Translation memory (TM). Previous translations of same/similar segments. Reused to maintain consistency and save cost.
Content segmentation. Break source into manageable segments. Usually sentence-level; sometimes finer.
Machine translation
Traditional NMT engines. DeepL, Google Translate, Microsoft Translator. Near-human quality on common language pairs.
LLM translation. Claude, GPT-4, Gemini all competent translators. Sometimes better for nuance and context; often more expensive.
Custom fine-tuned engines. For specialized domains (medical, legal, technical), fine-tuned MT outperforms general engines.
LLM post-editing of MT. Take NMT output; LLM refines. Captures benefits of both: NMT consistency plus LLM reasoning.
Human review
Post-editing. Human translator reviews MT output; fixes errors, refines style, handles cultural nuance.
Light post-editing (LPE). Fix major errors; don't aim for publication polish. Faster; cheaper; for internal or low-stakes content.
Full post-editing (FPE). Near-human quality output; longer review. For customer-facing or high-stakes content.
Cultural adaptation. Idioms, humor, references, examples — MT often misses. Human brings local expertise.
QA and delivery
Linguistic QA. Separate from post-editor. Checks against style guide, terminology, common errors. Second pair of eyes.
Automated checks. Tag consistency, number consistency, terminology compliance. Catches mechanical errors fast.
Client review. Client-side reviewer validates. For enterprise, often required by compliance.
TM update. Post-edited translations feed back into TM. Future projects benefit.
Cost/quality tradeoffs
Raw MT. Fastest, cheapest. Quality 70-85% depending on content type. Internal documents, low-stakes content only.
MT + light post-edit. 3-5x faster than human-only. Quality 85-92%. Suitable for most internal business use.
MT + full post-edit. 2x faster than human-only. Quality 95-99%. Suitable for customer-facing content.
Human-only translation. Reference quality; slowest, most expensive. Reserved for marketing, legal, creative, culturally sensitive.
Domain-specific considerations
Technical documentation. MT handles well; terminology consistency critical. Light post-edit often sufficient.
Marketing. Cultural adaptation matters enormously. Often treated as transcreation — partial recreation for target audience.
Legal. High stakes; terminology precise; often requires human translator familiar with jurisdiction.
Medical. Regulatory requirements may dictate workflows. Human certified translators often required.
Literary. Creative work; human translator considered essential. AI as research aide perhaps.
Tools
CAT tools. Trados, memoQ, XTM, Phrase. Core infrastructure for translation workflows. All integrate MT and TM.
TMS (translation management systems). Lokalise, Smartling, Crowdin for localization at scale.
LLM APIs. Used increasingly as post-editing aids or alternatives to traditional NMT.
Industry structure
Large LSPs (TransPerfect, Lionbridge, RWS). Enterprise clients; full-service workflows.
Mid-size LSPs. Regional or vertical specialists.
Freelance translators. Increasingly hybrid — MT-assisted post-editing work primary mode for many.
AI translation platforms. Unbabel, Phrase, others using AI-plus-human workflows with proprietary technology.
Future trajectory
LLM quality continuing to improve. Especially for low-resource language pairs.
Integration deeper. Translation embedded in content systems; less standalone workflow.
Human roles shifting. Less translation from scratch; more post-editing, QA, cultural adaptation, specialized domains.
See localization post for broader localization context.