AI offline evals fail in production for product managers (Hindi)

Discover why AI offline evals pass perfectly but your users still hate the product. Understanding the gap between AI offline evals and real performance is critical for product managers. When your model aces a static test set but fails in the app, you are experiencing the offline-to-online gap. This happens due to distribution shift, where real users type messy prompts your clean data never covered. Like a student who memorizes past exams but freezes in an interview, your model lacks context for real curveballs. To stop shipping disappointing features, you must test on the real road. Learn how to run shadow tests to log silent predictions on live traffic without affecting users. You will also learn to build a feedback loop by adding messy real prompts to your test set. In this lesson: - The offline-to-online gap and static test failures - How distribution shift breaks models with messy prompts - Running shadow tests to log silent predictions - Building a feedback loop to update your test set U2xAI Academy - AI skills for product managers. यह लेसन हिंदी में है. This lesson is narrated in Hindi.