Lecture Guide

Vibe Coding MVP Masterclass Overview

A beginner-oriented walkthrough of the four-hour loop from idea to shipped MVP.

Using Claude Code as the starting point, this course turns an idea into an MVP through a reusable loop covering documentation, stack choice, architecture, verification, and pre-launch review. It focuses on what beginners must define before prompting the AI.

This course does not treat generative AI as a tool that simply writes code for you. Instead, it explains how to use it as part of a working system for designing, building, and validating a small product quickly. Beginners often struggle more with sequence, documentation, and validation than with prompt wording.

You learn how to compress an idea into MVP scope, separate context through PRDs and AGENTS files, avoid overbuilt stack choices, and still end up with a structure that can be deployed. The real target is not one magical prompt, but a reusable personal working loop.

By the end, you should be able to explain and repeat a full flow that covers documentation, stack choice, architecture sketching, verification planning, and pre-launch review. The example assets focus on templates and structural examples rather than a giant codebase, making them easy to adapt into your own project.

Deliverable: a personal working loop for designing, building, and verifying an MVP with AI, plus reusable PRD/AGENTS templates, stack notes, an MVP architecture sketch, and a launch checklist.

Difficulty: Beginner to early intermediate
Estimated time: About 1h 30m
Format: 11-slide deck

Open slides

Who this is for

Beginner developers who have tried AI coding tools but have not yet turned them into a full project workflow.
Students, solo founders, and product experimenters who want to validate MVPs quickly.
Learners who want to understand that workflow, documentation, and verification matter more than clever prompts alone.

Prerequisites

A basic grounding in HTML, CSS, JavaScript, or React helps most.
It is fine if AI coding tools are new to you, but basic file/folder literacy is assumed.
It is fine if deployment platforms and doc editors are new to you, but knowing their role at a high level will make the flow easier to follow.

What you will be able to do

Use PRDs and AGENTS files to give AI a stable working context.
Choose a pragmatic tech stack and cloud cost envelope based on what the MVP actually needs to prove.
Connect implementation, UI/UX polish, testing, and deployment into one iterative loop.
Reduce pre-launch security review and critical manual checks into a compact working checklist.

Tools

Claude Code or a similar AI coding tool
A document editor
A Next.js or React-based MVP stack
A deployment platform such as Vercel, Cloudflare, or Netlify

Recommended study path

Read the PRD template and AGENTS example first; the slides become far easier to interpret.
For practice, follow the same sequence as the course: idea, documentation, stack choice, architecture, verification.
Do not aim for a perfect product on the first pass; focus on closing a small scope all the way to something you could realistically launch.

Chapter guide

Understand the philosophy and working loop

Frame vibe coding as a working system built around goals, principles, and deliverables rather than a code-generation trick. This chapter establishes the idea that humans set the rules and AI accelerates within them.

Many beginners treat vibe coding as little more than telling an AI to write code. In practice, the real productivity comes from defining the goal, setting constraints, and deciding when verification happens. That is why this chapter talks about targets and principles before code.

Vision decides what you are building, principles decide how you will build it, and deliverables decide what must exist for the work to count as done. Without those three anchors, AI can move fast but drift badly. With them, even an imperfect prompt can still live inside a stable workflow.

The course introduces a guardrailed working loop: define the target, pass context to the AI, implement in small units, verify with human judgment, and then iterate again after launch. The key idea is not that AI makes all decisions, but that humans keep the decision framework while AI increases speed.

The three pillars behind the vibe-coding loop
Pillar	Core question	What goes wrong without it
Vision	What are we trying to prove?	Features expand while the MVP purpose becomes vague
Principles	How will we move quickly without losing control?	The prompting style and execution rhythm become inconsistent
Deliverables	What proves the work is actually done?	Code exists, but documentation and release criteria do not

You will learn

Why vision, principles, and deliverables must be defined before prompting the AI
Why a guardrailed AI loop is more repeatable and safer

Key artifacts

Vibe coding loop diagram

Diagram

A one-page diagram of the five-step loop from target setting to AI direction, code generation, verification, and deploy/repeat.

Prepare the docs and context

Explain how AGENTS.md and PRDs divide responsibilities so the AI does not confuse operating rules with product requirements. The chapter also walks through a document structure beginners can reuse immediately.

One of the most common beginner frustrations is having to repeat the same context to the AI over and over. Most of that happens because document roles were never separated. AGENTS.md should carry operating rules and constraints, while the PRD should explain what the product must do and why it matters.

An AGENTS file holds information about how work should be done: style, scope limits, forbidden operations, and verification rules. A PRD holds what should be built: the user problem, success criteria, scope, flows, and priorities. If you mix the two, the AI reads rules and requirements as if they belonged to the same layer.

The chapter introduces document templates that even beginners can reuse immediately. The goal is not to write long documents for their own sake, but to create the minimum stable structure that keeps implementation and verification aligned later. The documentation is for the AI, but it is also a checkpoint system for the human builder.

Why AGENTS and PRDs should stay separate
Document	Question it answers	Typical contents	Failure mode when mixed
AGENTS.md	How should the work be done?	Rules, verification, prohibitions, style	The AI may over-prioritize rules or ignore them in favor of product work
PRD	What are we building and why?	Problem, success criteria, core flow, scope	Features expand while intent and priorities blur

You will learn

How to separate operating rules from product requirements
A reusable document template structure and writing order for beginners

Key artifacts

AGENTS.example(.md) operating-rules template

Template

A sample document for defining how the AI should work, what is disallowed, and how outputs should be verified.

PRD-template(.md) requirements template

Template

A lightweight PRD for defining the problem, success criteria, core flow, and scope so both humans and AI point at the same target.

Choose the stack and cost envelope

Choose the frontend, backend, and infrastructure based on the one thing the project must prove, then estimate a realistic MVP cost envelope. This chapter focuses on avoiding beginner over-engineering.

One of the most common beginner mistakes in MVP work is attaching too much technology to an idea that has not even been validated yet. Auth, real-time systems, microservices, and complex infrastructure may look impressive, but most MVPs only need to prove one thing first. This chapter asks not what is impressive, but what is sufficient.

The lecture shows how to narrow frontend, backend, and infrastructure choices based on project shape. A landing page, dashboard MVP, content-heavy product, and automation-first service all lead to different minimal stacks. Even when the same framework appears, the cost and complexity change depending on the actual problem it is solving.

Cost decisions are not just about comparing monthly bills. Managed cloud services often buy you speed, while self-hosted options buy you responsibility. For beginners, the more important question is not whether a tool is ten dollars cheaper, but whether it slows down implementation and validation at the stage where speed matters most.

A minimum-stack decision framework by project shape
Project shape	Preferred frontend	Preferred backend / data	Why this combination fits
Landing page + form	Astro or Next.js	Form backend / serverless	Fast launch and low structural complexity matter most
Dashboard MVP	Next.js	Supabase	Useful when auth and data operations must connect quickly
Content-centered service	Next.js or Astro	Headless CMS	Editing workflow and content reuse matter more than custom backend logic
Automation-heavy product	React or Next.js	Worker / queue / database	Async task flow matters more than the number of pages

You will learn

How to read the Skills vs MCP matrix, stack decision tree, and cloud cost matrix
How to avoid over-engineering an MVP and narrow the candidate stack

Key artifacts

stack-decision-matrix(.md) stack decision matrix

Decision matrix

A matrix for narrowing frontend and backend options by project shape. It serves as a starting point for deciding what is sufficient.

Minimum-stack guide by project shape

Framework

A compact guide for deciding which stack is minimal enough for landing pages, dashboards, content products, and automation-heavy tools.

MVP architecture and build flow

Use a restaurant-ordering MVP to explain how screen, API, and data layers can be split into a structure that is easy for both humans and AI to reason about. The chapter also shows how to slice implementation units for easier validation and iteration.

For beginners, the most dangerous architecture is not the most complex one, but the one that cannot be explained clearly. The same is true when working with AI. If screen, API, and data boundaries are vague, implementation slices blur and tests become harder to define. This chapter uses a three-tier MVP example to make those boundaries concrete.

The restaurant-ordering example separates the customer-facing UI, API routes or server actions, and the backend that manages orders, tables, and menu data. This keeps user-facing flows from being mixed with internal logic, and makes it much easier to explain one responsibility at a time to the AI.

The lecture also explains how to slice implementation work. Instead of asking for the whole ordering system at once, it is more effective to break the work into menu retrieval, order creation, status updates, and admin views. Smaller work units make AI output easier to inspect and make failures easier to fix. Small build slices also create better test slices.

A three-tier responsibility split using the restaurant-ordering MVP
Layer	What it owns	Example features	Good slice when working with AI
Screen layer	User experience and input flow	Menu display, cart, checkout view	Page-level or component-level slices
API layer	Validation, state transitions, server logic	Order creation, status update, admin actions	Endpoint-level or action-level slices
Data layer	Persistent data and read/write rules	orders, tables, menu, auth	Schema-level or query-level slices

You will learn

Where to split screen, API, and data layers so explanation and testing stay simple
How small your implementation units should be when working with AI

Key artifacts

mvp-architecture-example(.md) three-tier architecture example

Example doc

A worked example showing how to split customer UI, API logic, and the data layer so implementation and review stay understandable.

Verification, security, and launch

Turn a working MVP into a launchable one through verification and security checks. The chapter closes the loop by showing what to test first and which risks still need direct human review.

Many beginners assume the work is done once the screen renders and the API responds. In reality, just before launch you still need to confirm which end-to-end flows work for real users, whether secrets and permissions are safe, and how you would roll back quickly if something goes wrong. This chapter focuses on that release threshold.

Verification is not only about running tools. You also need to identify the highest-value user flow, pass it manually at least once, and leave enough logs and notes to trace failure when it breaks. This matters even more with AI-generated code, which often looks correct on the surface while still missing a critical edge case.

A security check does not mean a massive security audit. At the MVP stage, it is enough to reduce the biggest risks first: exposed secrets, weak permission boundaries, missing input validation, and unsafe external API usage. The lecture shows how to keep that checklist small and practical.

A minimum pre-launch review table
Checkpoint	What to verify directly	Where to look first if it fails
Core user flow	Does the main scenario complete end to end?	API boundaries, state changes, input validation
Secrets and permissions	Are env vars, admin paths, and deployment settings safe?	Deployment config, auth rules, client-exposed values
Release response	Do you have logs and a rollback path?	Deploy logs, monitoring, previous version recovery path

You will learn

What to test first and which scenarios still need direct human verification
The minimum security checklist and launch criteria before deployment

Key artifacts

Pre-launch verification and security checklist draft

Checklist

A release-time note covering core user flows, secrets, permission boundaries, logs, and rollback paths in the right review order.

Hands-on evidence

Guardrailed vibe-coding loop

A text version of the circular slide diagram so the core loop remains visible in the Lecture Guide.

Set target
   ↓
Direct the AI
   ↓
Generate code
   ↓
Verify & test
   ↓
Deploy & repeat
   ↺

AGENTS vs PRD split

Shows the minimum separation needed so operating rules and product requirements do not get mixed.

AGENTS.md → how the work is done
PRD       → what is built and why

Rules / guardrails stay here
Scope / success criteria stay there

Restaurant-ordering MVP 3-tier structure

The MVP architecture example repeated across the slide deck and supporting docs.

Customer UI (Next.js / React)
  -> API routes / server actions
  -> Supabase (orders, tables, menu)
  -> Admin dashboard / operations view

Practice assets

Example README

Document

A guide to the masterclass example files and how to use them.

AGENTS example

Template

A sample document for passing project operating rules to AI.

PRD template

Doc template

A compact template for turning an idea into implementation-ready requirements.

Stack decision matrix

Decision matrix

A matrix for narrowing tech choices based on project shape.

MVP architecture example

Architecture note

A text-plus-diagram example of a beginner-friendly three-tier MVP.

FAQ

Is this course tied to one specific AI coding tool?

Claude Code is the starting point, but the real focus is documentation and verification loops. Most of the method transfers to other AI coding tools because the course is really about the working structure, not the vendor name.

Should a beginner deploy an MVP right away?

Verification and guardrails come before launch. The course is about shipping fast without skipping the checks that matter, and it argues for closing a small scope safely rather than building something larger too early.

Are the example assets a large project?

No. Beginners benefit more from small reusable templates and structure than from a giant codebase, so the assets focus on those. They are intentionally sized so you can copy and adapt them into your own project quickly.