Prompt Engineering: The Translator's New Skill for the AI Era

In the spring of 2023, "prompt engineering" — a term from the AI geek world — moved to the center of the translation industry's attention. The reason is direct: the same large language model, fed different instructions, produces translations of wildly different quality.

Practitioner communities quickly distilled a methodology. A good translation prompt usually contains: role setting ("you are a senior legal translator"), context (purpose of the text, target readers, publication channel), style requirements (formal/colloquial, sentence-structure preferences), terminology constraints (attach a glossary, require strict adherence), and output format (preserve markup, segmentation rules). Compared with a bare "translate this into English", structured prompts produce output of a different order entirely.

More advanced usages include: having the model analyze the source style before translating, requesting multiple candidate translations with trade-off explanations, and asking the model to self-check terminology consistency. The essence of these techniques is "translating" the quality awareness in a translator's head into instructions an AI can execute.

The rise of prompt engineering reconfirms a judgment: the translator's value in the AI era lies not in out-converting the machine, but in knowing better than the machine "what a good translation is" — and injecting that judgment into the human-machine workflow.