A beginner-friendly course for anyone who wants to turn an idea into a working web product with AI, even without a traditional programming background. Using an AI-built Snake game and an e-commerce content workbench as running examples, you will learn how to narrow a problem, define an MVP, work effectively with AI, debug with evidence, use Git safely, set quality gates, deploy, and learn from real users.
Turn an initial request into one observable change, then complete a Vibe Coding feedback loop by running, observing, revising, and retesting it.
Choose a browser tool, AI IDE, or CLI based on setup cost, project context, control, and operational risk.
Turn a vague failure into a reproducible problem report and use matching runtime evidence to decide whether the fix is complete.
Rewrite a broad product idea as a testable problem statement centered on the gap between a user's current situation and desired outcome.
Select an accessible, testable first user group from a broad audience by looking for a shared situation, difficulty, and goal.
Use business questions to turn a vague product request into a core flow with input, processing, output, and essential failure feedback.
Define what the first version must include, what can wait, and what is explicitly out of scope around one validation goal.
Write acceptance criteria along the core flow that are executable, observable, decisively pass or fail, and cover critical states.
Explain a small web product through four layers—project files, runtime, saved data, and external services—and identify which layers a request affects.
Select and organize enough relevant context for a specific coding task so AI has less room to guess about the current state, scope, and done criteria.
Review an implementation plan for correct intent, bounded scope, sensible order, risk controls, stopping points, and verification before AI makes changes.
Describe product behavior through objects, properties, states, relationships, and actions, then use sample data to check whether the description is consistent.
Split a large requirement into a first vertical slice that connects user input, minimal processing, and a visible result.
Find breaks in a demo by following real use, then add the state feedback and persistence behavior required for a usable first version.
Use Git to establish a clear before-and-after boundary for an AI change and choose whether to commit, revise, or recover without overwriting the learner's existing work.
Diagnose a bug in the order of reproducible symptom, relevant evidence, one hypothesis, minimal change, and retest.
Derive a minimum quality bar from the product's promises and risks, then use it to decide whether to release or pause.
Explain and verify the minimum delivery path from a fixed code version through build and environment configuration to deployment, production retesting, and rollback.
Record behavioral facts during a specific user task, separate them from interpretations and suggestions, and turn feedback into the next testable question.
Organize project experience into a personal workflow for defining, planning, executing, verifying, and recording work, with check depth adjusted to task risk.
Decide whether a repeated process deserves to become a Skill and design the smallest structure that can trigger, execute, and validate correctly.
