mercor AI trainer
This project involved large-scale data annotation and evaluation to support the training and improvement of large language models for conversational AI. The scope of the project included reviewing AI-generated text responses and labeling them based on accuracy, relevance, reasoning quality, and adherence to annotation guidelines. Tasks included text classification, response ranking, error identification, and tagging outputs for factual correctness and safety. The project required consistent evaluation of datasets containing thousands of prompts and model responses across different topics such as general knowledge, technology, and everyday conversational queries. Annotators followed detailed labeling guidelines and used annotation tooling to ensure structured and consistent data tagging. Quality measures included regular guideline checks, consistency reviews, and adherence to predefined annotation standards to maintain high-quality labeled data. The annotated datasets were used to improve model performance, reduce hallucinations, and enhance the reliability of AI-generated responses.