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Glossary

The words we use, explained

AI training has its own vocabulary. Here is what each term means on SilkTrain.

AI training data
Examples and feedback that people create so an AI model learns to give correct, natural answers.
RLHF
Reinforcement learning from human feedback. People compare or score AI answers, and the model learns from their preferences.
Evaluation
Testing an AI model with prepared questions and checking how good its answers are.
Red-teaming
Deliberately trying to make an AI give harmful or wrong answers, to find and fix weak spots.
SFT data
Supervised fine-tuning data: questions paired with ideal answers, written by people.
Annotation
Labelling text, audio, images or video, for example marking names, topics or sentiment.
Gold set
Tasks with known correct answers, mixed into the work to measure each person’s accuracy.
QA score
A trainer’s accuracy on gold items over the last 30 days.
Tier 1, 2, 3
Levels of verification. Tier 1: automated tests passed. Tier 2: identity, proctored retest and work sample passed, ready to hire. Tier 3: 200+ paid hours with a QA score of 92% or more.
Probation
The first 10 paid hours on each new project, when all work is reviewed twice.
Proctored test
A test taken while the camera and screen are recorded, to make sure the person did the work themselves.
Work sample
A 20-minute recorded task with a spoken explanation, reviewed later by a native speaker.
Calibration
A session where the team learns the client’s guidelines before work starts.
Managed team
A team SilkTrain recruits and runs for a client, with a team lead and daily quality checks.
Self-serve marketplace
Where clients post jobs and hire vetted trainers directly. Opening soon.
Recruiting-as-a-service
SilkTrain finds and vets candidates who then work on the client’s own platform.
Pilot
A paid two-week trial with 10 vetted people, so a client can judge quality before committing.
Data localisation
Kazakhstan’s rule that personal data must be stored inside the country.
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