Ai Data Trainer
Text
Evaluation/RatingYes, I have experience working with guideline-based quality checks and audits. On the Feather platform, trainer tasks are strictly reviewed based on detailed guidelines, and there is a dedicated QC team that evaluates the quality of annotations. My work involves rating multiple AI-generated responses (usually 4 responses), comparing them side by side, and selecting the best one based on quality, accuracy, and guideline adherence. I also identify issues in each response, such as factual errors, language problems, or instruction-following mistakes, and note how many flaws are present. I consistently follow audit feedback provided by the QC team to improve my evaluations, ensuring better accuracy, consistency, and alignment with project standards.