There are moments when a small idea feels huge — an interface that should be simple, a dashboard that must tell a clear story.
Christopher Wolfe’s tarot app and a stock dashboard built with Copilot are practical proofs. They show how AI can scaffold a project fast while engineers keep final judgment. Claude Code, Vite, Material UI, Chart.js, Vitest, GitHub Actions, and AWS Amplify/CDK appear as part of those real workflows.
This guide lays out a clear path: start with intent, use AI as a co-pilot, and enforce tests and reviews so early speed does not become tech debt. We focus on delivering usable interfaces for the web that stay maintainable today and scale tomorrow.
Key Takeaways
- Use AI to accelerate scaffolding, but retain engineering oversight for quality.
- Move from idea to interface with clear prompts, acceptance criteria, and tests.
- Choose one UI system—Material UI or a utility framework—to keep consistency.
- Include CI/CD and reviews to prevent early velocity from creating debt.
- Learn from examples: tarot app pipelines and Copilot-built dashboards show common trade-offs.
For a practical toolkit and examples, see a concise write-up on vibe coding tools.
Set the stage: what vibe coding means for React development today
Fast scaffolding can deliver a working UI in hours; preserving quality takes deliberate steps. Teams that adopt a vibe coding approach seek momentum while guarding the long-term health of the codebase.
From idea to interface: balancing creativity with code quality
The Copilot-driven dashboard shows how quickly a directory, Vite + React + TypeScript scaffold, Material UI, and Chart.js can appear. That speed produces visible results fast, but it also exposes a key point: visuals alone don’t prove correctness.
Why AI co-pilots help, but engineering judgment still leads
AI proposals are useful defaults—they pick libraries, suggest themes, or add Vitest. Yet Wolfe’s tarot app shows risks: vague prompts, missing tests, and misinterpreted themes create rework.
- Momentum with guardrails: move quickly but demand tests and clear acceptance criteria.
- Review like a teammate: treat AI results as a junior PR—verify architecture, state flow, and features.
- Define “done”: engineers must state expected behavior, tests, and maintainability targets before merging.
Plan with intent: prompts, features, and framework choices before you code
Intent narrows choices and reduces rework. Begin with user stories that name actors, flows, and edge cases so generated code targets real behavior rather than guessing UI details.
Specify the stack early: scaffold a Vite + React + TypeScript project and pick one UI library—Material UI is common—and Chart.js for visuals. This single-framework approach keeps the application coherent.
Prompt patterns that work
- List features, chosen libraries, styling approach (styled-components or MUI system), and deployment target.
- Ask the agent to create specific file artifacts: README, .github workflows, infra templates, and test setup.
- Require a directory structure aligned to feature boundaries and a summary of implemented acceptance criteria.
- Request test scaffolding with Vitest and data-testids, and include realistic data and edge cases: empty states, API delays, and error boundaries.
Define “done” for each feature: pair acceptance criteria with unit and integration tests. Explicit bug reports and steps to reproduce help the agent fix issues and add tests before the next iteration.
vibe coding in React: a practical workflow from scaffold to first page
A working first page provides the clearest signal that your setup—tooling, theme, and tests—integrates correctly. Start by running npx create-vite@latest with the react-ts template and ask your agent to produce a feature-oriented file layout: components, pages, hooks, and services.
Immediately install Material UI and Chart.js and add a theme.ts that centralizes colors, type, and spacing. Commit to one CSS approach—MUI system or styled-components—and note the choice in a short style guide.
Test, CI, and deploy
Wire Vitest early and add data-testids to key elements; require a Home page smoke test and a shared component render test. Create GitHub Actions that run install, lint, type-check, tests, and build on each PR.
Prepare AWS Amplify hosting or a CDK S3+CloudFront deploy with a documented .env.example file. Add scripts for dev, build, preview, test, and lint so contributors run identical commands locally and in CI.
- Last step: capture decisions in an architecture doc—framework, UI system, theming, tests, and deployment—to guide future contributors.
For a practical look at agent-driven task assignments and project management, see a concise review here.
Keep the vibe without the mess: reviews, tests, and consistency
A disciplined review process turns fast AI scaffolds into reliable features. Teams should treat generated code like a junior pull request: inspect each line, confirm intent, and ask for clarifying notes when purpose is unclear.
Require tests with every change. Wolfe noted agents often skip tests until prompted and sometimes leave broken suites. Disallow merges when tests are absent or failing; this prevents silent regressions from accumulating.
Normalize refactoring as part of delivery. Simplify component trees, extract shared hooks, and remove redundant paths so the project remains maintainable without changing behavior.
- Benchmark render paths and identify reconciliation hotspots; apply memoization where it helps.
- Enforce a single UI library and theme tokens to avoid design drift across pages and components.
- Use static analysis: TypeScript strict mode, ESLint, and Prettier so teams debate architecture, not formatting.
Keep PRs small and frequent; reviewers give focused feedback and authors iterate faster. Add a short checklist for each PR: naming, prop types, accessibility, test coverage, and theme compliance.
For a compact set of practices that pair velocity with guardrails, see this concise write-up on vibe coding best practices.
Real-world examples: applying these steps to an app and a dashboard
Practical examples highlight where tight requirements save time and where vague asks cost effort.
Stock portfolio dashboard: prompts, theme, and build
The dashboard began with VS Code Copilot scaffolding a Vite + react + TypeScript project. Copilot suggested a directory file layout, installed Material UI and Chart.js, and produced a themed page with light and dark modes.
Result: a professional dashboard page with consistent spacing, typography, and color that built cleanly and rendered charts on the web.
Tarot application with CI/CD: tests, bugs, and cost over time
The tarot app used Claude Code and styled-components. After initial scaffolding, the team added Vitest tests and GitHub Actions, then deployed to AWS Amplify/CDK.
Bugs appeared—white screen on flip and blocked multi-card draws. The team filed explicit steps to reproduce and asked the agent to write failing tests first, then fix the code. This practice improved confidence for future changes and reduced regression risk.
- Lesson: tight prompts and one UI system cut rework; vague prompts raise iterations and file churn.
- Track cost and time—Wolfe reported $5.67 for scaffolding, tests, fixes, and a nav bar; repairs often cost more than the initial build.
- Start with a minimal working page, wire tests, and expand in small slices.
For a view on agent-driven workflows and team roles, see a practical write-up on agent-assisted development.
Conclusion
Conclusion
Scaffolding and themes accelerate delivery, yet lasting results require tests, reviews, and clear prompts.
AI reduced the time to a working page and a themed app, but Wolfe’s tarot work and the dashboard case remind teams to budget for stabilization. Prioritize test-first fixes, CI/CD, and concise prompts so generated files and lines of code are defensible.
Choose one UI system, keep design decisions documented, and run a quick “vibe check” before merging. Expect changes; treat AI as a force multiplier, not a substitute for engineering judgment.
For hands-on experiments and lessons learned, see the write-up on the great vibe coding experiment — a useful reference for projects that test prompts, feedback loops, and rapid iteration: the great vibe coding experiment.
FAQ
What does "vibe coding" mean for modern React projects?
Vibe coding is an approach that blends creative design intent with disciplined engineering. It emphasizes rapid experimentation, clear prompts, and repeatable scaffolds—while preserving code quality through tests, reviews, and defined acceptance criteria. Teams use tools like Vite, TypeScript, UI libraries, and CI/CD to turn ideas into production-ready pages without sacrificing maintainability.
How should teams set the stage before writing the first line of code?
Start with crisp requirements and user stories that an AI co-pilot or developer can act on. Choose the stack and libraries—Vite + React + TypeScript, a UI system such as Material UI, charting like Chart.js, and a CSS approach (styled-components or utility CSS). Define prompts for features, styling, and deployment and specify acceptance criteria and tests for each feature.
What role do AI co-pilots play, and where does engineering judgment still lead?
AI tools accelerate scaffolding, generate component drafts, and suggest tests or CSS patterns. Engineering judgment remains essential for architecture decisions, performance, security, and final code reviews. Treat AI output like a junior PR: verify purpose, edge cases, and adherence to design systems before merging.
How do you scaffold an app and directory structure effectively?
Use Vite to create a lightweight scaffold and enforce a predictable directory layout for pages, components, hooks, and tests. Include TypeScript types early, configure linting and formatting, and add a basic CI pipeline so builds and tests run on every push. This reduces drift and helps teams iterate on components and pages confidently.
Which UI and chart libraries work well for dashboards and apps?
Material UI is a reliable choice for consistent components and theming. For data visualization, Chart.js or Recharts provide flexible charting. Combine these with a single design system and theme tokens to avoid CSS drift and ensure a coherent visual language across pages and components.
What prompt patterns produce the best outputs for features and styling?
Use structured prompts: describe the feature, list required props and behaviors, specify libraries and styling approach, and include acceptance tests. For example: “Build a responsive portfolio chart using Chart.js, follow Material UI theming, include tooltips, and provide tests for data edge cases.” Clear prompts reduce rework and speed development.
When should tests be added and what kinds are most valuable?
Add tests as soon as a component has behavior or state: unit tests for logic with Vitest, integration tests for component interactions, and snapshot or visual checks for critical UI. Use data-testids to target elements reliably. Early tests provide fast feedback and lower the cost of change over time.
How do teams wire CI/CD for web deployment?
Implement GitHub Actions to run linting, tests, and builds on pull requests. For deployment, use AWS Amplify for straightforward hosting or AWS CDK for infrastructure-as-code when complexity grows. Automate previews to validate pages and features before production merges.
What is a practical workflow from scaffold to first page?
Scaffold with Vite, set up TypeScript and linting, install core libraries (MUI, Chart.js), create the first page with clear props and tests, and push a PR. Run CI to validate, review the code line-by-line, and merge once acceptance criteria and tests pass. This keeps momentum while preserving quality.
How should teams review AI-generated code to avoid messy merges?
Review it like a junior engineer’s PR: verify architecture alignment, test coverage, and naming. Check performance implications and accessibility. Refactor immediately where patterns are non-idiomatic and ensure a single UI system is used to prevent CSS and design drift.
What refactoring priorities improve maintainability and performance?
Focus on removing duplicated logic, simplifying component props, extracting custom hooks for shared behavior, and optimizing renders (memoization, stable keys). Measure with simple benchmarks and keep TypeScript types strict to catch regressions early.
Can you apply these steps to specific real-world apps like a stock dashboard?
Yes. For a stock portfolio dashboard, define prompts for charts, filters, and summaries; choose Chart.js and Material UI; implement tests for data aggregation and edge cases; and configure CI/CD to publish feature previews. This structure controls complexity and keeps feature delivery predictable.
How do you manage cost of changes and bug reports over time?
Track bugs and change requests in a backlog, enforce acceptance criteria for scope, and prioritize tests that capture past regressions. Consistent reviews and a stable design system reduce churn and lower long-term maintenance costs.
Which files and tooling should be present at project start?
Include package.json, tsconfig.json, Vite config, ESLint and Prettier configs, a README with prompts and workflows, and folders for src/pages, src/components, src/tests. Add GitHub Actions workflows for CI and a basic deployment config for your chosen hosting provider.
