I am an entry-level AI evaluation and data annotation professional with hands-on experience reviewing large language model outputs and applying structured quality criteria. I assess responses for accuracy, relevance, completeness, reasoning, clarity, reliability, and adherence to instructions.
I identify hallucinations, fabricated evidence, unsupported claims, ambiguity, reasoning errors, and overconfident conclusions. I use independent evidence checks and structured reports to document findings and support quality-focused decisions.
I have practical training in image and video data annotation, including object classification, bounding boxes, polygons, masks, semantic segmentation, instance segmentation, and keypoint labeling. I focus on consistent, evidence-based labels while accurately distinguishing visible, occluded, and truncated objects.
My background in Journalism and Mass Communication strengthens my research, writing, media analysis, critical thinking, and communication abilities. I bring a detail-oriented approach to evaluating information quality and presenting findings clearly.
I am also developing AI fluency and prompt engineering capabilities through projects involving prompt chaining, task decomposition, tool selection, structured LLM workflows, and human verification. I maintain an AI evaluation portfolio that documents rubrics, evidence-grounding checks, severity assessments, and quality-assurance methodologies.
Evaluate large language model responses for accuracy, relevance, completeness, reasoning, clarity, reliability, and instruction following.
Identify hallucinations, fabricated evidence, unsupported claims, reasoning errors, ambiguity, and overconfidence. Compare outputs with independent evidence and document findings in structured evaluation reports.
Apply image and video annotation guidelines for object classification, bounding boxes, polygons, masks, semantic segmentation, instance segmentation, and keypoint labeling.
Practice consistent, evidence-based labeling with an emphasis on accuracy and dataset quality. Distinguish visible, occluded, and truncated objects without making unsupported estimates about hidden content.
Supported media operations during the World Rafting Championship in a time-sensitive event environment.
Contributed to event media activities and communication tasks as part of the media team.
Bachelor of Arts, Journalism and Mass Communication
Relevant areas include research, media analysis, written communication, critical analysis, presentation, and academic writing.
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