Prompt Engineering
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Fine-tuningOver the past three years, I have actively engaged in prompt engineering across multiple AI platforms including large language models and image generation tools. My work has involved designing, testing, and iteratively refining prompts to produce accurate, high-quality AI outputs across text and visual content. A core part of my process includes output evaluation and correction, critically reviewing AI-generated responses for accuracy, coherence, and completeness, then reprompting with improved instructions when results fall short. For image generation specifically, I identify and resolve issues such as missing details, incorrect visual elements, and prompt misinterpretation, iterating until the final output meets the desired standard. This hands-on experience has developed my understanding of model behavior, prompt structure, context framing, and fine-tuning principles, skills directly applicable to AI training and data labeling workflows. While self-directed, this work reflects the same core competencies required in professional AI data annotation: attention to detail, quality control, and the ability to evaluate and improve model output systematically.