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The best AI evals course for most product managers is the Evals chapter of AllthingsPM's AI PM course, at $108 a year with a free tier. For live depth, Hamel Husain and Shreya Shankar's Maven course ($4,500); for a free technical primer, DeepLearning.AI's Evaluating AI Agents.
A golden dataset is a small, reviewed set of real inputs with agreed pass criteria that you run on every change to your AI feature. Start with 20 to 50 cases drawn from real failures, label them pass or fail, and grow the set from production. AllthingsPM teaches the full method in its Evals chapter.
An AI benchmark is a fixed test set with a scoring rule. Read it honestly by checking what it measures, how it was run, whether it is contaminated or saturated, and how big the error bars are, then test on your own tasks. AllthingsPM teaches this in its AI PM course.
LLM observability for product managers means reading traces: the tree of model calls, tool calls and retrievals behind one user request. This guide shows how to read one, what to look for, and how to turn 100 traces into a ranked failure backlog, with AllthingsPM course lessons for each step.
AI evals are the tests that decide whether an AI feature is good enough to ship. This guide gives PMs the full method: trace reading, failure taxonomy, golden sets, LLM judges and launch gates.
Real AI product manager interview questions from OpenAI, Anthropic, Sierra, Google, Meta and more, grouped by the areas 2026 loops test, with strong-answer outlines. Practice every one in a scored mock on AllthingsPM, which has 4,122 real questions from 260 companies.
Reading is the easy half.
The course grades the other half.
