Vibe Coding: AI-Powered App Development for Product Managers

Overview

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This course shows product managers how to turn AI from a buzzword into a hands‑on build partner. By the end, you’ll be able to design and ship real applications by “vibe coding” with AI. Describing outcomes, flows, and emotional tone in natural language while the model handles code, architecture, and integration. You’ll learn how to use Model Context Protocol (MCP) to make your agents predictable and safe, engineer context so they reflect your product’s voice and guardrails, and run fast, AI‑assisted iterations from first prototype through testing and retrospective. Drawing on real case studies like a PTSD support app and a four‑day health platform, you’ll practice shipping useful, user‑centered AI products without needing deep engineering skills. This course is practical for product managers who want to compress timelines, validate ideas quickly, and stay in control of experience and ethics while AI does the heavy lifting.

Syllabus

Module 1: Vibe Coding

Discover how “vibe coding” lets product managers build real apps by talking to AI instead of writing code. Explore a real-world health app built in four days, unpack the five core principles of vibe coding, and see how to focus on outcomes, emotion, and user experience rather than syntax.

Module 2: Model-Context-Protocol (MCP)

Move beyond basic prompting and learn how to design AI agents using the Model Context Protocol (MCP). See how leading companies use MCP to turn large language models into reliable, reusable “AI brains” that power real workflows.

Module 3: Context Engineering

Learn how to move from ad‑hoc prompting into context engineering so your AI-powered apps behave consistently and safely at scale. Design the “brains” of your vibe‑coded apps and explore memory design, tone and vibe guardrails, and success criteria.

Module 4: AI-Assisted Product Management

Take vibe‑coded apps from first build to tested, refined, and launch‑ready. Learn how to co‑develop with AI through rapid iteration, give effective feedback when features miss the mark, and how to close with a lightweight retrospective.