Professional translation is expensive and slow. Machine translation is fast and cheap but produces inconsistent terminology and misses domain-specific nuance. The best approach combines both: AI handles the heavy lifting while humans review and refine.

The Problem

Organizations expanding internationally need to translate product documentation, marketing materials, legal documents, and support content. Professional translation costs $0.10-0.30 per word and takes days. Machine translation is instant but produces output that needs significant editing for professional use.

The AI Approach

An LLM-based translation pipeline can produce high-quality first drafts that incorporate your specific terminology glossary. By providing domain context and a glossary of approved term translations, the output is significantly more consistent than generic machine translation.

Three-Step Build

Step 1 - Glossary preparation. Build a terminology glossary mapping key terms to their approved translations in each target language. Include 50-100 domain-specific terms that generic translation handles poorly.

Step 2 - AI translation. Send source documents to an LLM with the glossary and instructions to preserve formatting. For longer documents, chunk by section to stay within context limits while maintaining coherence.

Step 3 - Review routing. Flag sections where the model expressed low confidence or used terms not in the glossary. Route these to a human translator for review while auto-approving high-confidence sections.

Where It Breaks

Legal and regulatory documents require certified human translation regardless of AI quality. Highly creative content (marketing copy, slogans) needs cultural adaptation, not literal translation. Languages with limited training data produce lower quality output.

The Production Path

Measure post-editing effort (changes per sentence) to quantify quality improvement over time. Build translation memory from human corrections to reduce repeat work. Integrate with your CMS to trigger translation workflows automatically when source content is updated.