The AI revolution has shifted with the emergence of Large Language Models (LLMs) like ChatGPT, indicating a victory over data complexity. These LLMs face challenges due to massive datasets (up to a petabyte) and the intricate nature of human language. Data-centric tools like Cleanlab have revolutionized AI data handling, automating data improvement processes and democratizing advancements. Data-centric AI is essential due to annotation errors (7-50%) in real-world datasets, hampering training. OpenAI and tools like Cleanlab prioritize data quality, allowing significant improvements in model accuracy. A Python demo showcases how Cleanlab detects label issues, filters data, and re-trains models, resulting in a remarkable accuracy increase from 63% to over 66%. This data-centric approach holds promise for future LLMs like GPT-5.