← All lessons
Browse lessons

Week 6: Decision-Making and Organization · Lesson 6.3

Prioritization and iteration using evaluation evidence

What should we fix first, and how do we learn fast?

Retired course. Due to the fast pace of AI, this course was retired before full release. Exercises, datasets, and videos referenced in this lesson are not available. The slide content and frameworks remain free to study.

Slide 1 of 20

Reader Notes

Week 6, Lesson 3. The full evaluation system is built. The hard work is done. SQL errors, retrieval failures, policy violations are all identified and backed by evidence from Weeks 1 through 5. This is where most teams stumble. They know what to fix but not which to fix first. That is the gap between knowing and shipping. In the last lesson, a quick three-factor priority score was used: impact times confidence divided by effort. That works for ranking within one set of findings. But when managing a full evaluation backlog with dozens of competing improvements, more dimensions are needed. This lesson's framework adds user harm, frequency, time-to-learn, and strategic alignment to produce a complete prioritization model. This lesson teaches how to convert a messy backlog of known issues into a ranked iteration plan where every work item has clear acceptance criteria and every ranking is defensible with evidence. No more arguing about opinions. The result is a systematic method.

Go deeper with AI Analytics for Builders

5-week course: metrics, root cause analysis, experimentation, and storytelling. Think like a Product Data Scientist.

Book 1-on-1 with Shane

30-minute AI evals Q&A. Talk through your specific evaluation challenges and get hands-on guidance.

Finished all 36 lessons? Take the exam and get your free AI Evals certification.