The article explains how AI product managers have shifted from creating prototype diagrams to designing continuous evaluation “exams”, using real‑world examples, data‑driven testing, cross‑team collaboration, and iterative error analysis to deliver truly useful AI products.
This article explains the GM(1,1) grey prediction model, covering data preprocessing, model construction, parameter estimation, error testing methods, and how to generate forecasts, providing a practical guide for applying the technique to time‑series data.
This article explains why diagnostic analytics is essential, defines the concept, outlines problem types, presents a logical‑tree and hypothesis‑driven methodology, showcases two real‑world projects (weather‑impact index and an intelligent anomaly‑diagnosis system), and highlights frequent mistakes to avoid.
This article presents a systematic analysis of how thread numbers, request counts, response times, and response‑time variance affect performance‑testing error rates, using a Java demo that simulates single‑API calls and reports detailed JSON metrics for each configuration.
This article explains how to extend a performance testing framework with new QPS and deviation fields, implement accurate QPS calculations for both fixed‑thread and fixed‑QPS models, and prepare the system for quantitative error analysis using detailed Java code examples.
This article examines the two common QPS formulas, presents a simple single‑thread request model, identifies theoretical and practical sources of error such as pre‑ and post‑request processing, logging overhead, and real‑time data handling, and discusses how to mitigate these inaccuracies in performance testing.
