Technical Translation: Human, Google Translate or LLM?

For ai technical translation, the short answer is a hybrid workflow. The machine handles volume, a human reviewer who knows your industry validates what matters, and the depth of review follows the risk of the document. A safety manual deserves different care from a sales flyer. Here is how the three options compare.

The specialist human translator

Quality: highest, especially when the professional knows your industry and the target market's terminology. They catch ambiguity in the source and adapt units, standards and conventions.

Cost and speed: the most expensive and slowest of the three, especially for big catalogs and frequent updates. Risk: low, but dependent on the individual's quality and on a good glossary.

Classic machine translation

Quality: good enough to grasp the general meaning, but it often trips on technical terms, abbreviations and long sentences, and can render a word wrongly without any warning. Consistency across documents is another weak spot.

Cost and speed: nearly free and instant. Risk: medium to high for text that will be published, and there is also the matter of sending confidential documents through free services.

Large language models

Quality: usually more fluent than classic machine translation, and the big advantage is that they accept instructions: use this glossary, keep units, keep a formal tone. That improves terminology.

Cost: low per document. Risk: the model may rewrite, drop a sentence or round a value while the text still sounds right, which is dangerous with torque, tolerances and safety warnings.

Side by side, by criterion

• Technical accuracy: specialist human, then LLM with glossary, then classic translator.
• Speed: classic translator and LLM, far ahead of a human.
• Cost per page: classic and LLM low; human higher.
• Term consistency: all three depend on a glossary, but only human and LLM apply it well.
• Confidentiality: automatic tools need a business plan or private deployment.

The recommended hybrid flow

1. Build a bilingual glossary of your product terms and have engineering approve it.
2. Sort documents by risk: high (safety, installation, contracts), medium (manuals, datasheets) and low (blog and social content).
3. Use an LLM with the glossary for the first draft.
4. Specialist human review in full for high-risk documents and by sampling for medium.
5. Feed the reviewer's corrections back into the glossary so the next batch starts better.
6. Run a check on numbers and units, partly automatable.

Exporters gain a lot from digital catalogs in several languages, and a flow like this keeps every version current without prohibitive cost.

Warning signs

Be suspicious when a translation reads too polished and too short, when numbers change format between languages, or when the same term appears translated two ways in one manual. In those cases, go back to the glossary and the review step.

Frequently asked questions

Can I publish AI translation without review?

For low-risk content such as posts, perhaps. For manuals, datasheets and safety notices, specialist review is recommended.

Do I still need a glossary when using AI?

Yes. It makes sure your product terms are always rendered the same way and noticeably improves the model's output.