Build a Batch Translation Tool in an Afternoon
Before building any pipeline, prove quality with a single request. Send one sentence with a clear instruction that names the target language and asks for the translation only.
Chunk long documents ✂️
Models have a context limit and quality drops when a single request is too long. Split documents on paragraph or sentence boundaries and keep a stable identifier for each chunk so you can reassemble them in order.
Pipeline stages 🚰
| Stage | Action | Tip |
|---|---|---|
| 1 | Chunk | Never split in the middle of a sentence |
| 2 | Translate | Send one chunk per request with a fixed instruction |
| 3 | Validate | Check output length and language of the reply |
| 4 | Reassemble | Join chunks with the original separators |
| 5 | Review | Spot-check a sample, keep the rest automated |
Keep terminology consistent 📚
Glossaries matter more than raw fluency for product names and legal text. Put your preferred terms in the system instruction and reuse the exact same instruction for every chunk so the style does not drift.
Make failures obvious 🚨
- Reject empty or suspiciously short outputs and retry once
- Log the chunk id, target language and model for every call
- Store the source and translation side by side for auditing
- Count tokens per run so a translation budget never surprises you
Frequently asked questions ❓
Which model should I use for translation?
A fast, general chat model handles most content well. Reserve a larger or reasoning model for languages or domains where quality matters most.
How big should each chunk be?
A few paragraphs is a practical default. Smaller chunks are easier to review and retry; larger chunks keep more context.
Can I translate a whole website at once?
Batch it. Send chunks in parallel with a small concurrency limit so you neither overload the API nor wait too long.
How do I catch a bad translation?
Validate the reply language and length automatically, then have a human review a random sample and anything the checks flag.
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