This article demonstrates how data-centric AI tools like cleanlab can improve a fine-tuned Large Language Model (LLM) by optimizing the dataset itself, rather than altering the model architecture or hyperparameters. The authors use the Davinci model from OpenAI and achieve a 37% boost in test-set performance on a politeness classification task after removing data with automatically-flagged label issues and then fine-tuning the LLM on the filtered dataset. Similar gains are achieved for other state-of-the-art LLM models, Ada and Curie. The authors also introduce a no-code solution to efficiently fix label errors in the dataset using Cleanlab Studio, which reduces the error rate of the model by 37%. The article highlights the benefits of data-centric AI tools like cleanlab, which can help systematically engineer better data via automation, freeing up time for domain experts to focus on their unique knowledge.