Why tools like ChatGPT, Gemini and DeepL aren’t built for translating large documents or databases

In recent years, tools like DeepL and ChatGPT have made translating short texts incredibly easy. For emails, product descriptions, or quick understanding of a foreign language, they’re almost indispensable.
But when it comes to large-scale content – technical manuals, online databases, catalogs, legal documentation, or XML/HTML files – their limitations quickly become obvious. These tools simply aren’t designed for serious, high-volume translation projects.

1. Size Matters: AI Doesn’t Like Big Files

Every AI tool has limits on how much text it can process at once. DeepL only allows a certain number of characters per translation, and ChatGPT starts “forgetting” previous content after a certain point.

When you try to feed too much content into these systems, here’s what happens:

  • Only part of the text gets translated
  • Sections disappear or cut off mid-sentence
  • The system crashes or throws errors
  • Some content simply gets lost

The result? You end up manually splitting documents into smaller chunks, which takes time and creates inconsistencies, especially in catalogs and structured data.

2. Formatting and Structure? Gone.

It’s not just about words. Formatting matters too. DeepL and ChatGPT translate plain text – they don’t reliably preserve headings, tables, lists, or layout.

For technical documents or catalogs, where structure is critical (think tables with dimensions, numbers, notes, or references), losing formatting can make the translation unusable. Even if the language is correct, you’ll spend hours reconstructing the original layout.

The result: the speed AI promises disappears in manual rework.

3. More Manual Work, Less Automation

AI translation looks automated at first glance: paste the text, get the translation. But for large documents, it’s the opposite: tons of manual effort.

You’ll need to:

  • Split text into smaller parts
  • Track character limits
  • Merge translated chunks back together
  • Check sequence consistency
  • Fix structural and coding errors manually

For databases (product names, descriptions, technical specs), this work doubles – every row must match the original, or you’ll end up with broken data and mismatched entries.

4. The Risk of Breaking XML and HTML Tags

Technical and web content often includes tags like <p>, <h1>, <strong>, <br>, <li> or custom XML codes that control display and functionality.

DeepL and ChatGPT don’t always handle these correctly. Common issues include:

  • Dropping tags entirely
  • Moving them to the wrong place
  • Translating them as plain text

The result? Invalid files that can’t be imported back into your system. Fixing these errors manually is time-consuming and complex, especially when dealing with thousands of XML lines.

Conclusion

Tools like DeepL, Gemini and ChatGPT are fantastic for short texts and quick language checks. But for large documents, extensive databases, or technically structured content, their limitation are clear: size restrictions, lost context, broken formatting, incorrect tags, too much manual work.

For projects like these, the smarter choice is professional translation systems – solutions designed to handle large volumes, preserve technical structure, and automate processes without endless manual fixes.

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