Self-Learning (Generative AI Data Annotation)
Through ongoing self-directed learning and practice in Generative AI Foundations, I focused on data quality for AI models by applying annotation expertise to sample datasets. My work emphasized consistency and bias reduction, demonstrating the crucial role of accurate data in AI model performance. The process included prompt engineering, responsible AI use, and hands-on text data evaluation for large language models. • Implemented data annotation best practices while curating AI datasets for training and evaluation. • Developed and validated prompts supporting model effectiveness and ethical AI outcomes. • Ensured continuous personal development in generative AI, LLMs, and prompt engineering. • Contributed to improved dataset integrity for generative AI experimentation.