The Precision-Recall (PR) Curve is a fundamental diagnostic tool for evaluating AI model performance, particularly crucial when handling imbalanced datasets. Understanding LLM key performance metrics is vital in real-world applications like fraud detection, where finding patterns and striking a critical balance between identifying genuine threats and avoiding false alarms are essential. Precision measures the correctness of a classifier's positive predictions, while recall expresses how many actual positive cases a model correctly predicted. Assessing recall performance is also crucial in medical diagnostics, ensuring that the most true cases of a disease are detected. PR Curves offer a nuanced view of performance across several domains, including healthcare, finance, and content moderation. Implementing precision-recall metrics effectively presents significant challenges, such as data quality assurance, ground truth verification, and maintaining consistent performance at scale. Modern solutions like Galileo's Evaluate module tackle these issues through advanced data validation techniques and AI-assisted verification processes. The AUC-PR metric distills a curve's performance into a single number, making it easier to compare different models. Effective classifiers often result in high AUC-PR scores, but considering context-specific performance indicators is essential to match model goals. Balancing precision and recall in machine learning and AI models requires implementing effective strategies, such as those outlined by Galileo, which help achieve optimal precision-recall balance aligned with operational requirements.