Aether Patch
On the Aether project with Outlier, I contribute to AI training and evaluation by analyzing model-generated responses and improving their quality through structured feedback and precise annotations. My role involves reviewing prompts and outputs, identifying inaccuracies or logical gaps, applying detailed rating criteria, and providing corrections that help refine model performance. I follow strict guidelines to ensure consistency, clarity, and alignment with project standards across large-scale datasets. I also perform quality control tasks, compare alternative responses, and help fine-tune outputs for accuracy, coherence, and factual reliability. This work requires strong critical thinking, attention to detail, and the ability to interpret complex instructions quickly. Through this project, I directly support the continuous improvement of large language models by ensuring high-quality training data and evaluation standards.