Translation quality is influenced by a wide range of technical, linguistic and organisational factors. In addition to the quality of the source text, terminology, company knowledge, contextual information, the AI model used and suitable quality assurance measures play a particularly important role. Only the interaction of these factors enables consistent, technically accurate and target-group-specific translations.
Even high-performance AI systems can only deliver results that are as good as the information provided allows. Unclear wording, ambiguous technical terms or missing specifications regarding terminology, style and target audience increase the risk of mistranslations and unnecessary post-editing. Conversely, a structured data basis significantly improves quality as early as the first translation draft.
The most important influencing factors
- Source text quality: Clear, unambiguous and well-structured source texts form the basis for precise and comprehensible translations.
- Terminology and company knowledge: Glossaries, style guides, reference documents and translation memories help ensure that technical terms, brand language and company-specific wording are used consistently.
- Contextual information: Information about the target audience, document type, intended use or regulatory requirements enables a more precise interpretation of the source text.
- AI model and translation technology: Depending on the language model, subject area and language combination, the quality, style and consistency of the translation may vary.
- Quality assurance: AI-supported quality checks, as well as expert review by translators or subject matter experts, help to identify and correct linguistic, terminological and content-related errors at an early stage.
✅Example from practice
An engineering company translates a technical operating manual into several languages. By using a well-maintained terminology glossary, a style guide and existing translation memories, the AI recognises the technical context and uses all safety-relevant terms consistently. Subsequent quality checks ensure that the translation meets both linguistic and technical requirements.
✅Good to know
Many quality issues arise before the actual translation begins. A clearly worded source text, along with well-maintained terminology, company knowledge and unambiguous style specifications, often improves translation quality more than switching to a more powerful AI model.