I have experience supporting AI training and data labeling projects involving prompt writing, output review, and quality evaluation for AI-generated images and videos. My work has focused on checking whether outputs match instructions, identifying missing or incorrect details, comparing multiple results, and improving prompt wording to achieve more accurate and consistent generations. I pay close attention to visual quality, prompt adherence, composition, object accuracy, style consistency, and overall relevance.
I am comfortable working with detailed guidelines, making consistent labeling decisions, and handling repetitive review tasks with accuracy and focus. My strengths include attention to detail, clear judgment, quality control, and the ability to document observations in a structured way. Through these projects, I have built practical skills in data annotation, content evaluation, prompt testing, and AI output review. I am well suited for tasks that require precision, consistency, and careful interpretation of instructions across large sets of training data.
Intermediate
Labeling Experience
AI Image and Video Output Evaluation
ImageEvaluation Rating
I worked on AI training and data labeling tasks focused on evaluating AI-generated images and videos for quality, accuracy, and prompt adherence. Reviewed outputs against detailed instructions to determine whether the generated content matched requested objects, scenes, style, composition, and visual details. Assessed results for consistency, clarity, realism, relevance, and overall output quality.
Responsibilities included comparing multiple outputs, identifying defects or missing elements, flagging inaccurate generations, and documenting observations according to project guidelines. Applied consistent judgment across tasks involving content review, annotation, and quality evaluation, while paying close attention to fine details such as object placement, background accuracy, visual coherence, and alignment with prompt requirements.
This work strengthened my skills in data annotation, AI output evaluation, quality assurance, and instruction-based review. It also required careful decision-making, consistency across large batches of tasks, and the ability to follow structured labeling standards in a fast-changing generative AI environment.
I worked on AI training and data labeling tasks focused on evaluating AI-generated images and videos for quality, accuracy, and prompt adherence. Reviewed outputs against detailed instructions to determine whether the generated content matched requested objects, scenes, style, composition, and visual details. Assessed results for consistency, clarity, realism, relevance, and overall output quality.
Responsibilities included comparing multiple outputs, identifying defects or missing elements, flagging inaccurate generations, and documenting observations according to project guidelines. Applied consistent judgment across tasks involving content review, annotation, and quality evaluation, while paying close attention to fine details such as object placement, background accuracy, visual coherence, and alignment with prompt requirements.
This work strengthened my skills in data annotation, AI output evaluation, quality assurance, and instruction-based review. It also required careful decision-making, consistency across large batches of tasks, and the ability to follow structured labeling standards in a fast-changing generative AI environment.
2025 - 2026
data annotation specialist
VideoPrompt Response Writing SFT
I worked on several AI training and data labeling projects focused on evaluating and improving AI-generated images and videos. My role involved writing and refining prompts, reviewing outputs carefully, and judging how well the results matched the given instructions. I assessed content for prompt accuracy, visual consistency, object placement, composition, style, scene details, and overall quality. This required strong attention to detail and the ability to notice small differences between what was requested and what the model produced.
A major part of the work involved comparing multiple outputs, identifying defects or inconsistencies, and selecting the best result based on project guidelines. I regularly flagged issues such as missing elements, inaccurate details, weak composition, poor alignment with the prompt, and low-quality visual rendering. I also improved prompt wording to help produce more precise and relevant outputs, which strengthened my ability to understand how instruction phrasing affects AI performance.
Across these projects, I followed structured guidelines closely and applied labeling standards consistently across repeated tasks. I became comfortable making careful judgment calls on nuanced factors such as realism, mood, clarity, background accuracy, and adherence to specific requested attributes. I also documented observations clearly and worked in a detail-oriented way to support accuracy and quality control.
This experience helped me build practical skills in data annotation, content evaluation, quality assurance, prompt testing, and instruction-based review. It also strengthened my consistency, focus, and ability to work effectively on repetitive tasks that require accuracy and careful decision-making. Overall, these projects gave me solid hands-on experience with AI training workflows and reinforced my ability to contribute to labeling, review, and quality improvement tasks.
I worked on several AI training and data labeling projects focused on evaluating and improving AI-generated images and videos. My role involved writing and refining prompts, reviewing outputs carefully, and judging how well the results matched the given instructions. I assessed content for prompt accuracy, visual consistency, object placement, composition, style, scene details, and overall quality. This required strong attention to detail and the ability to notice small differences between what was requested and what the model produced.
A major part of the work involved comparing multiple outputs, identifying defects or inconsistencies, and selecting the best result based on project guidelines. I regularly flagged issues such as missing elements, inaccurate details, weak composition, poor alignment with the prompt, and low-quality visual rendering. I also improved prompt wording to help produce more precise and relevant outputs, which strengthened my ability to understand how instruction phrasing affects AI performance.
Across these projects, I followed structured guidelines closely and applied labeling standards consistently across repeated tasks. I became comfortable making careful judgment calls on nuanced factors such as realism, mood, clarity, background accuracy, and adherence to specific requested attributes. I also documented observations clearly and worked in a detail-oriented way to support accuracy and quality control.
This experience helped me build practical skills in data annotation, content evaluation, quality assurance, prompt testing, and instruction-based review. It also strengthened my consistency, focus, and ability to work effectively on repetitive tasks that require accuracy and careful decision-making. Overall, these projects gave me solid hands-on experience with AI training workflows and reinforced my ability to contribute to labeling, review, and quality improvement tasks.
2025 - 2026
Education
No Education added yet
Foluso L. hasn’t added any Education History to their OpenTrain profile yet.
Work History
No Work History added yet
Foluso L. hasn’t added any Work History to their OpenTrain profile yet.
