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ImageI am an experienced data annotator and AI training data specialist with a strong background in creating high-quality labels and prompts for large language models (LLMs) and agentic AI systems. My work includes crafting detailed annotations, roleplaying as AI agents to simulate user interactions, developing and grading rubrics for prompt evaluation, and performing rigorous quality assurance across STEM and generalist domains. I have hands-on experience with prompt engineering, model failure analysis, and instructional content creation for projects involving language understanding, database navigation, and web page interaction. My attention to detail, multilingual abilities (English, Chinese, and Japanese), and familiarity with tools like Google Workspace and Microsoft Office ensure precise and efficient data labeling. I thrive in collaborative environments, often mentoring peers and contributing workflow improvements to enhance project outcomes.
Breaking down ideal AI responses into criteria that an AI model should follow when answering a user's specific prompt. Rated responses against each other once the criteria was created, and explained why.
Taught an AI how to handle math. Includes adjusting step-by-step process, creating math problems, and making sure that they fit a high-school or an undergraduate-level difficulty.
We taught an AI how to navigate the internet, told the AI the logic and actions that a human would take, and then also noted down important key facts that were pertinent to the user's requests.
The project involved using creative writing to come up with how a user may interact with an AI to do everyday, common tasks. The specific data annotations that were performed was writing out how a human logic works to the AI, what actions to take, and helping the AI report back to the user with the things that they need.
Bachelor of Arts and Sciences, Psychology and Economics
High School Diploma, General Education
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