Short answer: human-in-the-loop means real people label your training data and judge your model's outputs. Small teams can get this without hiring in-house by using a service like TestLancer, which offers AI data annotation (text, image, video, audio), image labelling, AI response evaluation and consented, human-made data collection.
What is AI data annotation?
It's adding labels to raw data so a model can learn from it: tagging text, marking objects in images, transcribing or classifying audio, or labelling video.
What does image labelling involve?
Drawing boxes or polygons around objects and assigning classes. Quality matters more than volume: inconsistent labels teach a model inconsistent behaviour.
What is AI response evaluation?
People rate and rank model outputs. It's how you find out whether answers are actually helpful, accurate and safe, things automated metrics often miss.
Why use consented, human-made data collection?
When you need new data, you want to know where it came from and that the people involved agreed to provide it.
Tips for running a human evaluation round
- Write clear guidelines with examples of good and bad outputs.
- Start with a small pilot batch and review disagreements.
- Use more than one rater per item where it matters.
- Feed the findings back into prompts, data or fine-tuning, then re-test.
What else do AI products need from humans?
AI apps are still apps. You'll also want real-device testing, usability testing and real user feedback. TestLancer covers mobile and website testing, QA (functional, regression, exploratory), UX research and surveys too.
FAQ
Who does the work on TestLancer? Real people. Testers and freelancers join free and are paid per test.
Can I hire someone ongoing? Yes, you can hire vetted freelancers and virtual assistants.
Where do I start? https://testlancer.com/
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