A clear-headed breakdown of LLM translation: what they excel at (speed, volume, pattern recognition) and where they systematically fail (domain accuracy, compliance precision, legal accountability).
- What the model is actually doing
- Where it genuinely performs
- Where it systematically fails
- The practical conclusion
What the model is actually doing
A large language model produces the most likely continuation based on its training data and your input. When the phrasing you need is well represented in that data, the output is fluent and often correct. That covers a great deal of routine content.
What the model is not doing is checking. It has no representation of whether a parameter is right for your device, whether a term matches your approved labelling, or whether a claim is one you are permitted to make. Fluency and correctness are produced by the same mechanism, which is why incorrect output does not look incorrect.
Where it genuinely performs
High-volume repetitive content, internal reference material, first drafts of well-trodden text, and situations where an error costs someone thirty seconds of confusion. In these cases AI removes real cost and time, and insisting on full human production is waste.
It also performs well as a starting point for expert work. A specialist editing a solid draft is faster than the same specialist starting from nothing — which is exactly how we use it.
Where it systematically fails
Three failure modes recur. Domain accuracy: the model cannot verify that a technical term is correct in your specific context. Compliance precision: regulated wording is defined by a standard and by your existing filings, not by what is most probable. Accountability: every major AI provider disclaims liability for output, so when something goes wrong there is no one on the other side of the failure.
These are not bugs awaiting a better model. They follow from what the system is — a probability engine with no stake in the outcome.
The practical conclusion
The useful question is not "AI or human" but "what is the cost of being wrong here". Low-stakes content should be cheap and fast. Content where an error reaches a regulator, an operator or a court needs someone accountable reading it. Our position is deliberate: AI raises efficiency, experts ensure quality, and Angel takes responsibility for what is delivered.