VIBE Coding: Teach the Workflow, Not (Just) the Language

Background & assumptions. Coding has shifted from “type every line by hand” to “design, prompt, and iterate with an AI pair-engineer.” Schools still teach syntax-first; companies still over-index on frameworks. The missing skill is a repeatable LLM-first workflow that turns intent into shippable software with tests, docs, and guardrails. its called VIBE Coding a practical way to build with AI that students and teams can adopt today.

What is VIBE? Vision → Iterate → Build → Evaluate. You set a clear product vision, iterate with an LLM to produce modular plans and code, build section-by-section, and continuously evaluate with tests, reviews, and telemetry. It’s not “codes by vibes.” It’s a structured conversation loop that optimizes for speed and reliability.

Why schools & companies should switch

• Closer to how modern software would be shipped. Teams will increasingly co-create with coding assistants; graduates would focus on the workflow.
• Faster learning curve. You practice architectural thinking, testing, and prompting i.e. transferable across stacks.
• Built-in safety. VIBE could include coding best practices e.g. version control, high-level tests, and doc-grounding from day one.

Pick your tools & make a plan

Front-end

• Replit (instant browser IDE, great for teaching/prototyping): https://replit.com
• Lovable (AI app builder/IDE): https://lovable.dev

Back-end & AI pair programing

• Cursor (LLM-native IDE): https://cursor.com
• Windsurf (Codeium’s AI IDE): https://codeium.com/windsurf
• Claude Code (Anthropic’s coding experience): https://www.anthropic.com/news/claude-code
• Gemini Code Assist (Google’s enterprise code assistant): https://cloud.google.com/gemini/docs/code-assist

Make the plan (with an LLM)

Ask your assistant to propose a modular, sectioned plan before any code is written.

Prompt template example: “Act as a senior engineer. Produce a modular plan for a <app/feature>. Sections must be: Goals, User stories, Architecture, Data model, API design, Test strategy (high-level), Risks/assumptions. Constrain to . Output a numbered outline where each section is independent and shippable.”

Then, proceed section-by-section: “Now implement Section 2 only, following the plan. Include tests and a brief README for the module.”

Use version control

• Any Git works (GitHub, GitLab, Bitbucket). Core docs: https://git-scm.com/doc
• Yes, LLMs keep conversation context, but Git is your source of truth for diffs, reviews, and rollbacks.
• Keep short branches per module; open small PRs (Pull Requests) mapped to the plan’s sections, it helps breaking down larger changes into more manageable, self-contained units.

Write tests (favor high-level)

• Prioritize high-level test cases (acceptance/spec tests) over low-level micro-unit tests early on; they validate user value and keep you honest during refactors.
• Example: Web/app testing: Playwright https://playwright.dev
• Example: Python backends: pytest https://docs.pytest.org
• Ask the LLM to: “Generate acceptance tests for user stories 1–3; avoid coupling to internal function names.”

Build guardrails: request property-based tests for tricky logic, negative tests for auth/validation, and smoke tests for deployment.

Remember: LLMs aren’t just for coding

• Example 1 — Configure a DNS server: Have the model draft BIND9 configs and a validation checklist, then compare against official refs. BIND docs: https://bind9.readthedocs.io
• Example 2 — One-click hosting on Heroku: Ask for a Procfile (Heroku Procfile is a plain text file), app.json, and health check endpoint. Heroku Dev Center: https://devcenter.heroku.com

Use the LLM to also write runbooks, on-call checklists, and deployment scripts.

Bug fixes

• Paste failing tests and logs; ask the LLM for a minimal patch / fixes.
• If the conversation gets noisy, start fresh with a clean prompt containing only the failure + current file(s).
• Validate with your test suite before merging.

Documentation

• Ground the model with real docs using MCP (Model Context Protocol) for tool/doc integration where available: https://modelcontextprotocol.io
• Alternatively, store PDFs/links locally and tell the LLM what to cite when generating code or explanations. Keep a /docs folder and a top-level DECISIONS.md.

Functionality

• Start fresh, module by module. Each module should ship with tests, a README, and examples.
• Or bootstrap from modular open-source (license-compatible), then guide the LLM to tailor and harden it. Always review licenses and headers before reuse (GitHub has license metadata: https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/licensing-a-repository).

Choose the correct stack

• Pick stacks that maximize developer speed with AI assistants.
• Example: Ruby on Rails for CRUD-heavy apps (fast scaffolding, conventions): https://rubyonrails.org
• Pair with a universal front-end approach (e.g., React in Replit) when needed.

Refactor frequently

• Ask: “Propose refactors that reduce complexity by 20% without changing behavior; show before/after diffs.”
• Run your high-level tests to keep regressions out while you simplify.

Keep experimenting

• Tools evolve quickly; schedule short spikes to try new assistants/agents every sprint.
• Track outcomes in PLAYBOOK.md: prompts that worked, failure modes, eval results, and “gotchas.”

A 1-week VIBE Sprint (school or company)

1. Day 1 (Vision): Draft the modular plan with the LLM; agree on scope & tests.
2. Day 2 (Iterate): Implement Section 1 (backend skeleton + health checks) with tests.
3. Day 3 (Build): Implement Section 2 (core feature, data model); open PRs; review.
4. Day 4 (Evaluate): Add acceptance tests, deploy to staging (e.g., Heroku), write runbook.
5. Day 5 (Refactor & Docs): Refactor with guardrails, finalize docs, demo, and retro into PLAYBOOK.md.

What to unlearn from traditional courses

• Syntax drills without testing & deployment context.
• Monolithic “big bang” projects; prefer modular sections.
• Treating AI as a cheat; instead, treat it as a force multiplier inside tight guardrails.

Call to action

• Schools: Replace week-long syntax labs with VIBE sprints that end in a deployed, tested module.
• Companies: Make VIBE the default onboarding track; one sprint is enough to shift habits.
• Everyone: Teach prompting as an engineering skill, not a party trick.

Resources (official)

• Replit — https://replit.com
• Lovable — https://lovable.dev
• Cursor — https://cursor.com
• Windsurf — https://codeium.com/windsurf
• Claude Code — https://www.anthropic.com/news/claude-code
• Gemini Code Assist — https://cloud.google.com/gemini/docs/code-assist
• Git — https://git-scm.com/doc
• Playwright — https://playwright.dev
• pytest — https://docs.pytest.org
• BIND9 DNS — https://bind9.readthedocs.io
• Heroku Dev Center — https://devcenter.heroku.com
• Model Context Protocol — https://modelcontextprotocol.io
• Ruby on Rails — https://rubyonrails.org

Bottom line: VIBE Coding turns AI from “autocomplete” into a disciplined way of shipping real software. If we teach the workflow i.e. Vision, Iterate, Build, Evaluate; students and teams will learn to engineer with AI, not around it.