Review an AI-translated work document systematically instead of checking only whether it sounds natural. Use a small synthetic example to compare numbers, units, names, negations, and selected back-translations against the source.
Who this is forPeople who use an AI chat assistant to translate work documents and need a repeatable way to verify important details before sharing them.
An AI translation can read smoothly while changing an important fact. A decimal point may move, a unit may disappear, a product name may be translated unnecessarily, or a negative instruction may become positive. These errors are especially risky in technical, purchasing, scheduling, compliance, and operational documents because a small wording change can alter what someone is expected to do.
The example below is synthetic. The goal is not to judge literary quality. It is to verify whether information that must remain stable across languages has actually remained stable.
Assume the English source contains a short instruction for a fictional test team. The target language is not important for this exercise; the verification process is the same.
| Source item | Source text |
|---|---|
| Quantity | Prepare 12 samples. |
| Torque | Tighten Bolt A to 35 N·m. |
| Temperature | Do not exceed 80 °C. |
| Date | Send the report by October 14, 2026. |
| Name | Send the final file to Mina Park. |
| Product name | Use the Alpha-X bracket. |
The critical values are therefore 12, 35 N·m, 80 °C, October 14, 2026, Mina Park, and Alpha-X. The phrase 'Do not exceed' is also critical because reversing or weakening the negation changes the operating instruction.
When requesting the translation, specify which items must remain unchanged or must not be interpreted creatively.
Even with a careful prompt, verification is still required. The prompt reduces risk; it does not prove that every critical detail survived the translation.
| Translation issue | Result |
|---|---|
| 35 N·m translated as 35 Nm | Meaning may still be understandable, but verify whether the document requires exact unit notation. |
| 80 °C translated as 80 °F | Incorrect unless an explicit unit conversion was requested and checked. |
| Mina Park translated or respelled | Check against the authoritative spelling. |
| Do not exceed 80 °C translated as Maintain 80 °C | Incorrect because the prohibition and meaning changed. |
| Alpha-X translated into a descriptive phrase | Incorrect if Alpha-X is the official product name. |
A useful second check is to take selected translated sentences and ask the AI to translate them back into the source language without seeing the original. This is especially useful for negative instructions, commitments, deadlines, and sentences where the wording feels unusually different.
Suppose the back-translation returns 'Keep the temperature at 80 °C' instead of 'Do not exceed 80 °C.' That difference is a warning that the translated instruction should be rechecked. However, a matching back-translation still does not prove correctness because the same AI may reproduce its earlier interpretation.
A small script can help detect obvious changes to protected details. The following standard-library example reads source.txt and translation.txt, extracts numbers and selected protected strings, and writes a report to outputs/translation_check_report.txt. It does not modify either original file and stops if the outputs folder already exists.
from pathlib import Path
import re
import sys
SOURCE_PATH = Path("source.txt")
TRANSLATION_PATH = Path("translation.txt")
OUTPUT_DIR = Path("outputs")
REPORT_PATH = OUTPUT_DIR / "translation_check_report.txt"
if not SOURCE_PATH.is_file():
sys.exit("Missing input file: source.txt")
if not TRANSLATION_PATH.is_file():
sys.exit("Missing input file: translation.txt")
if OUTPUT_DIR.exists():
sys.exit("Stop: outputs folder already exists. Remove or rename it manually first.")
source = SOURCE_PATH.read_text(encoding="utf-8")
translation = TRANSLATION_PATH.read_text(encoding="utf-8")
number_pattern = re.compile(r"\b\d+(?:\.\d+)?\b")
source_numbers = number_pattern.findall(source)
translation_numbers = number_pattern.findall(translation)
protected_strings = [
"N·m",
"°C",
"Mina Park",
"Alpha-X",
]
lines = []
lines.append("AI TRANSLATION CHECK")
lines.append("====================")
lines.append("")
lines.append(f"Source numbers: {source_numbers}")
lines.append(f"Translation numbers: {translation_numbers}")
if source_numbers == translation_numbers:
lines.append("PASS: number sequence matches")
else:
lines.append("CHECK: number sequence differs")
lines.append("")
lines.append("Protected strings:")
for item in protected_strings:
in_source = item in source
in_translation = item in translation
if in_source and in_translation:
lines.append(f"PASS: {item}")
elif in_source:
lines.append(f"CHECK: missing or changed in translation -> {item}")
lines.append("")
lines.append("Manual checks still required:")
lines.append("- Verify negations and obligation strength sentence by sentence.")
lines.append("- Confirm that each number still refers to the same item.")
lines.append("- Check dates, names, units, and product names in context.")
lines.append("- Spot-check high-risk sentences with back-translation.")
OUTPUT_DIR.mkdir()
REPORT_PATH.write_text("\n".join(lines) + "\n", encoding="utf-8")
print(f"Created: {REPORT_PATH}")
Run the script with python translation_check.py after saving the two texts. Matching number lists are useful but not sufficient. The translation could contain the same numbers in the wrong sentences, so every flagged or critical item still needs contextual review.
Automated checks are strongest at finding visible mismatches. They are much weaker at verifying whether a sentence preserves intent, scope, exceptions, responsibility, or legal effect. Those require contextual reading and, for high-stakes documents, appropriate subject-matter review.
The practical rule is to treat an AI translation as a draft whose critical facts must be verified independently. For contracts, safety instructions, regulatory documents, medical content, or other high-consequence material, use the organization's required professional review process rather than relying on a lightweight AI and script check.
2026-09-21 · hand-checked example · Python 3.12
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