Core Insights - The focus of AI is shifting from problem-solving to problem-definition, emphasizing the importance of evaluation over training [1][4] - The evaluation process is a continuous practice that drives development and requires a scientific approach [7][10] Evaluation Framework - Building a product evaluation system is fundamentally about applying the scientific method, involving a cycle of questioning, experimentation, and analysis [8] - Initial steps include observing data, examining inputs, outputs, and user interactions to identify operational strengths and weaknesses [8] - Data labeling is crucial, prioritizing problematic outputs to create a balanced and representative dataset for targeted evaluation [8] Hypothesis and Experimentation - Formulating hypotheses about errors is essential, which may involve analyzing retrieval documents and reasoning paths [9] - Designing experiments to validate these hypotheses is necessary, including rewriting prompts or updating retrieval components [9] - Measuring results quantitatively is critical to determine the effectiveness of changes made during experiments [9] Evaluation-Driven Development (EDD) - EDD helps create better AI products by defining success criteria through product evaluation before development begins [12] - The process involves establishing baseline evaluations and continuously assessing each adjustment to ensure measurable progress [12] - EDD fosters a feedback loop that is rooted in software engineering practices, ensuring that improvements are based on objective data [12] Automation and Human Oversight - Automated evaluation tools enhance monitoring but cannot replace human oversight; regular sampling and analysis of user feedback are still necessary [14][15] - High-quality labeled data is essential for calibrating automated tools to align with human judgment [14] - Maintaining a feedback loop of data sampling, output labeling, and tool optimization is crucial for effective evaluation [14][15]