Large language models (LLMs) have progressed significantly, yet opportunities remain to enhance their reasoning and accuracy. Labelbox has introduced two new features aimed at improving the quality and reliability of LLM training: fact-checking and prompt rating. The fact-checking feature allows evaluators to break down complex responses into smaller parts, enabling more precise accuracy assessments and corrections, while the prompt rating feature helps identify and skip prompts that do not meet predefined criteria. These tools facilitate the generation of high-quality data, improve model understanding, and bridge the gap between human and machine intelligence by incorporating human feedback into the learning process. Additionally, these features contribute to refining reasoning and ensuring the model learns from its mistakes, ultimately leading to more accurate and reliable outputs.