Data annotator
Data annotation & labelling- Tagging or structuring text, images, or other data so AI models can learn from it. Prompt creation & response writing- Writing high-quality prompts and ideal responses to train models on how to answer questions accurately and naturally. Evaluation & ranking- Comparing multiple AI-generated responses and ranking them based on usefulness, correctness, and clarity. Error analysis- Identifying where the AI made mistakes (e.g., factual errors, bias, poor reasoning) and explaining why. Rewriting & editing outputs- Improving AI-generated content to meet higher standards of accuracy, tone, and coherence. Domain-flexible tasks- Working across topics like general knowledge, coding, math, or creative writing depending on project needs. Quality assurance adhered to included; Accuracy- All information must be factually correct and verifiable. Clarity & coherence- Responses should be easy to understand, well-structured, and logically consistent. Instruction adherence- following task guidelines exactly (format, tone, constraints). Consistency- handling similar tasks in a uniform way across different examples. Bias & safety awareness- Avoiding harmful, biased, or inappropriate content; flag issues when necessary. Attention to detail- Small mistakes (grammar, labeling errors) can reduce dataset quality. Efficiency with quality balance- Meeting productivity targets without sacrificing accuracy.