Trados Studio 2026 shipped in July with Language Weaver Pro baked in, plus connectors for ChatGPT, Claude, and DeepSeek. memoQ's 12.x line lets you write your own AI prompt inside the editor. The old divide — your workspace here, a machine translation plugin over there — is gone. The LLM now sits in the segment, filling it before your cursor arrives.
And here's the thing nobody in the launch copy says out loud: the draft reads well now. That's not a feature. For QA, it's the whole problem.
Clunky used to be a gift
Back when raw MT gave you a stiff, obviously-machine sentence, the errors announced themselves. Wrong register, broken agreement, a term translated three different ways in four segments — you could feel it with your eyes half closed. The ugliness was a flag. You slowed down because the text told you to.
An LLM doesn't give you that. It hands you a smooth, confident Turkmen sentence that happens to use the wrong term, and the smoothness is exactly what buries the error. Tomedes put a number on the shape of this: a term that's plausible but incorrect is harder to catch when the surrounding prose flows. I'd go further. Fluency is a form of camouflage. The better the sentence sounds, the less your brain argues with it.
I hit this on an oil & gas job last month. The model rendered a casing specification in Turkmen that scanned beautifully — grammatical, natural word order, the kind of sentence I'd be happy to have written. It had quietly swapped the completion term for a drilling one. If the draft had been clumsy, I'd have rebuilt the sentence from scratch and caught it on the way. Because it was clean, I almost signed off on a lie that read like the truth.
Where Turkmen makes it worse
For my pair the fluency trap is sharper than for, say, Spanish. The models have seen oceans of Spanish. They've seen a puddle of Turkmen. So the output lands in an odd place: fluent enough to disarm you, under-trained enough to be wrong on anything that matters.
The agglutinative suffix stacking is where it shows. The model picks a root term, then attaches case and possessive suffixes that are grammatically flawless — vowel harmony perfect, the lot — around a word that's simply the wrong choice. So the error isn't a grammar glitch my checker would scream about. It's a lexical decision, correctly inflected. My terminology checker is looking for a string match; it sees the right morphology wrapped around the wrong stem and waves it through. The QA panel is clean. The translation is not.
That gap — clean panel, wrong meaning — is the new center of gravity in this work. It's why I don't trust a green QA report on an LLM-seeded project the way I'd trust one on a translation I built myself. The automated check and the actual risk have drifted apart.
What I actually do about it
First, I stopped treating the pre-fill as a draft to polish. It's a proposal to interrogate. The order of operations matters here. If I read the Turkmen first, I inherit its confidence. So I read the English, decide what the term should be, and only then look at what the model put down. Source-first, every segment where the stakes are real. Slower? Yes. But the fluent-wrong error only gets caught by someone who formed their own answer before seeing the machine's.
Second, the termbase does more work than it used to, not less. When an LLM sits in the editor, a locked term isn't a convenience — it's a guardrail against exactly the plausible-substitute problem. I front-load terminology on these jobs. Every project term that has a single correct Turkmen equivalent goes in before I translate a word, so the moment the model deviates I've got a hard reference to check against, not a vibe.
Third, I price the review differently. MTPE on a fluent draft is not faster post-editing of a clunky draft. The physical typing drops, sure. The cognitive load of spotting confident wrongness goes up. Anyone quoting LLM post-editing as if it's cheaper because the sentences look finished is quoting the wrong thing. The surface got easier. The judgment got harder. That's the part you're actually paying a human for.
Tomedes reports over 70% of their projects now start with AI pre-translation. I believe it — my intake looks the same. The workflow has already changed under us. What hasn't changed is who's accountable when a clean-reading sentence ships the wrong meaning into a legal filing or a wellsite procedure.
The machine got better at sounding right. It did not get better at being right in Turkmen. And those two things used to travel together, which was the only reason the old QA ever worked.