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Created by Shaunak Ghosh
Interns learn to spot hidden assumptions in vague engineering requests, convert unknowns into the right clarifying questions, and close the loop with clear acceptance criteria. You’ll practice on realistic prompts like “fix the bug” and “add a search feature” to avoid building the wrong thing.
5 modules • Each builds on the previous one
Learn why vague tasks trigger hidden assumptions, and how wrong assumptions create expensive rework. You’ll practice turning an assumption into an explicit question before coding.
Learn a quick scan to label what’s ambiguous, what information is missing, and what you’re assuming. This creates a short “clarification checklist” before you message a mentor or PM.
Use a small set of question categories (goal, user, scope, constraints, edge cases, priority, and success metrics) to generate high-quality clarifying questions fast, without sounding unprepared.
Learn how to summarize what you heard into a lightweight spec: scope boundaries, decisions made, and acceptance criteria. This closes the loop so everyone agrees on what 'done' means.
Apply the full workflow to two common vague tasks: “fix the bug” and “add a search feature.” You’ll practice spotting unclear parts, listing assumptions, asking top questions, and writing a confirmation summary.
Begin your learning journey
In-video quizzes and scaffolded content to maximize retention.