What is Post-Editing?
Post-editing (still often referred to as machine translation post-editing, or MTPE/PEMT) involves reviewing a translation generated by a machine like Google Translate or the AI of large language models like ChatGPT and refining it to improve accuracy, clarity and tone. It's different from revision, review, and proofreading in that the aim is not always to produce a perfect text. A post-edit might involve just tweaking a few awkward phrases so the text makes sense, or it could mean a significant rewrite to make the translation read as if it were written by a human.
The rise of AI tools like ChatGPT, Gemini and Claude has dramatically improved machine translation quality since the first attempts back in 1933. But they still can't match human translation, and this is where post-editing comes in.
Post-editing is not always the best option, especially for creative or highly nuanced content, which usually benefits from being translated from scratch. But when time and budget are the top priorities, having a human post-edit output from tools like DeepL or ChatGPT can be a fast, cost-effective solution.
What's the difference between machine translation and AI translation?
What's the difference between machine translation and AI translation?
Neural Machine Translation systems are trained specifically for translation and tend to make predictable errors, such as awkward phrasing or overly literal translations, which are usually easy to spot and fix. The AI of LLMs, by contrast, is trained on vast amounts of text to generate human-like language across a wide range of tasks, with translation being just one of them.
While LLMs often handle context better than traditional MT systems and generally produce more fluent, natural-sounding output, they still struggle with nuance, idioms, cultural references and implied meaning. The result is often text that sounds polished but doesn't quite capture the original message or tone. More worryingly, LLMs can introduce subtler errors, changing meanings, inventing details or leaving out information that was in the source. This means a ChatGPT translation might actually need more careful editing than one produced by a tool like Google Translate.
Although both technologies rely on neural networks, they behave differently, and each calls for its own type of post-editing.
Levels of post-editing
I offer three levels of post-editing to suit different needs. These edits can also be applied to Dutch-to-English translations written by non-native speakers of English.
Light post-edit – Minimal intervention to ensure the text is comprehensible. Best suited for internal or short-lived content. Typically only needed for machine-translated text rather than output from LLMs.
Fixes major spelling, grammar and punctuation errors
Ensures everything is translated and removes obvious mistranslations
Does not include stylistic improvements or terminology consistency checks
Standard post-edit – A more polished version for short-term or external content. Usually recommended for machine translation, but LLM output will often also benefit from a standard post-edit. Includes all the checks in a light post-edit, plus:
Fixes major and minor spelling, grammar and punctuation errors
Makes minor stylistic changes to improve readability
Ensures key terminology is used correctly (if a glossary is provided)
Full post-edit – A high-quality, human-like translation. Recommended for LLM-generated content and any text intended for long-term use. Includes all the checks in a light and standard post-edit, plus:
Refines structure, tone, flow and localisation
Rewrites sections for clarity and natural style
Ensures consistency and accuracy across the text
Includes a full manual check for spelling and grammar
Can include fact checks on request
