Why AI Translation Loses Consistency
Generic LLM translation of long documents drifts in three predictable ways: terminology, tone, and structure. The causes are architectural, not stylistic.
Three root causes
- Stateless chunks: each chunk is translated without memory of how earlier chunks rendered the same term.
- No glossary enforcement: the model sees the term fresh each time and chooses a plausible rendering for that local context only.
- No register analysis: with no chosen framework, tone drifts between chunks as the model interpolates from local cues.
What fixes it
An orchestration layer that performs register analysis once, builds a glossary once, and then passes both into every chunk's instruction. The chunks remain stateless to the model; the orchestration carries the state.
Further reading: glossary strategy, register foundations, how the workflow holds state.