In short
A useful evaluation set is held out from training, reflects real usage, includes known hard cases and past failures, and is refreshed as the product improves.
Teams keep a fixed benchmark for a year, watch the score rise and then get surprised by user complaints. The benchmark stopped measuring anything once the product saturated it.
What belongs in the set
- Real user inputs, not invented ones
- Every production failure, added permanently
- Deliberately hard and ambiguous cases
- Coverage of every group and language the product serves
Protect it
Evaluation data must stay separate from production preparation. That means checking for overlap with the working corpus, not just trusting the split, since the same text often arrives from two sources.
Refresh as you improve
Retire items everyone passes and add new failures. An evaluation set should stay uncomfortable to be worth running.
Practical checklist
- Define the acceptance rule before any volume starts
- Review a small pilot before committing the full budget
- Track errors by category, language and reviewer
- Keep consent, source notes and version history with the files
Before you ask for a quote
A clear brief saves days. Share a sample file, target language or region, expected volume, deadline, quality threshold and any privacy restrictions. A supplier can then price the work on real effort rather than assumptions.
- Which languages, markets or user groups must be represented?
- What format does the final file need to arrive in?
- Who will approve ambiguous cases during the pilot?
- evaluation
- testing
- quality
Need this done rather than read about it?
We run collection, annotation, transcription and localization projects for teams who would rather spend their time on the product.
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