Vibe Coding: A Practical Playbook for Building Faster with AI
- Staff Desk
- 3 days ago
- 7 min read
Vibe coding is emerging as a powerful way to ship software quickly by pairing a programmer’s judgment with capable AI coding tools. The core idea is simple: apply the same discipline that strong engineers use, but collaborate with language models to plan, implement, debug, and refine. This blog distills the key practices and tips from a YC talk and founder experiences into a clear, reusable workflow.
What Is Vibe Coding?
Vibe coding treats natural language as a first-class “programming language.” Instead of writing only code, developers describe intent, constraints, and steps in plain language and let AI help generate or modify code. Results improve when the process mirrors professional engineering: plan first, build incrementally, test end to end, keep versions clean, and avoid compounding mistakes.
Where to Start: Picking the Right Tool
Beginner-friendly options
Replit and Lovable offer approachable, visual environments. They’re strong for rapid UI prototyping and trying ideas directly in code.
Limitation to watch: as backend logic grows, purely visual tools can make unexpected changes. A button tweak shouldn’t rewrite unrelated server logic.
Engineer-oriented tools
If there’s prior coding experience, tools like Windsurf, Cursor, or Claude Code provide deeper control across frontend and backend in the same project.
Each tool has strengths. Anecdotally, Cursor can feel faster for full-stack iterations, while Windsurf may offer more thoughtful, longer reasoning.
A practical tactic
Keep two agents open on the same project. Submit the same change to both with shared context and compare outputs. Choose the iteration that reads cleaner, integrates better, or passes tests first.
Plan Before Coding: Treat AI as a Co-Planner
Write a plan with the LLM
Collaborate with the model to draft a comprehensive plan and save it as a PLAN.md in the repo.
Edit the plan manually: remove features that aren’t essential, mark “won’t do” items, and keep a backlog of “ideas for later.”
Work section by section
Ask the AI to focus on a single section, implement it, and stop.
Run tests, verify behavior, and commit.
Mark that section “complete” in the plan, then move to the next.
Why this works
Whole-product “one-shot” generations often wander, invent files, or break invariants. A staged plan keeps the codebase coherent and makes rollbacks cheap.
Version Control: Keep a Clean History
Use Git religiously
Start each feature branch from a clean state.
If the AI’s suggestions go sideways, perform a hard reset to the last known good commit and try again with a fresh prompt.
Avoid layer-cake drift
Re-prompting to patch a broken fix on top of another patch tends to accumulate “crust.” Once a correct solution is found, reset and reapply that solution cleanly.
Commit checkpoints
Commit after each working step. This creates stable “safe zones” for experimentation and makes it easy to bisect regressions.
Tests First, and Keep Them High-Level
Start with tests
Handcraft test cases for new features. They act as guardrails for the LLM.
Favor high-level integration and E2E tests over narrow unit tests. Simulate real user flows: clicking through pages, submitting forms, verifying outcomes.
Why it matters
LLMs sometimes change unrelated logic. A robust integration suite flags accidental regressions immediately and reduces debugging time.
When Tools Stall: Practical Workarounds
Switch surfaces
If the in-IDE agent is stuck or looping, paste the same code and question directly into the model’s web UI. The change in context often helps the model escape dead ends.
Try multiple models
Different models excel at different tasks: planning, long-context navigation, implementation, or debugging. If progress stalls, switch models and re-ask the question.
Debugging: Short Loops, Fresh Starts
First response to a bug
Paste the exact error message into the LLM. Server logs and browser console traces are often enough for a quick, precise fix.
For complex failures
Ask the model to propose 3–4 plausible root causes before writing any code. Decide which hypothesis to test first.
After a failed attempt, reset to a clean state and try the next hypothesis. Avoid stacking speculative edits.
Add logging early
When symptoms aren’t clear, add or enhance logging. Then present those logs to the LLM to ground its reasoning in runtime facts.
Write Agent Rules and Local Docs
Instruction files
Most tools support project-level instructions (e.g., Cursor Rules, Windsurf Rules). Capture conventions, architecture decisions, tech stack notes, and testing expectations here.
Detailed rules increase consistency and reduce hallucinations.
Local documentation
Online docs ingestion is still hit-or-miss. Download relevant API docs into a /docs folder and point the agent there. Instruct the model to “read local docs first.”
Use AI as a teacher
After generating code, ask the LLM to explain it line by line. This accelerates learning and makes future prompts more precise.
Isolate Complex Features Before Integrating
Reference implementations
For a tricky feature, create a standalone mini-project or find a clean reference repo. Get the feature working there first.
Then ask the LLM to replicate the pattern inside the main codebase, following the reference while adhering to local conventions and tests.
Small files, strong boundaries
Prefer modular, service-like components with clear APIs and minimal coupling. This helps both humans and LLMs reason about changes safely.
Architecture That Helps AI Help You
Modularity beats monoliths
Large monorepos with tangled dependencies make it hard for the model to predict side effects. Clear interfaces and small modules reduce surprises.
Stable interfaces
Maintain consistent external APIs so internals can be refactored freely. If tests still pass and interfaces remain steady, the codebase stays healthy.
Choosing a Tech Stack the Models Understand
Mature frameworks shine
Conventions and abundant examples improve model output quality. Ruby on Rails is a good example: 20 years of consistent patterns and “the Rails way” help models place code correctly.
Newer or niche languages (e.g., Rust, Elixir) may work, but results can vary where public training data is thinner.
Practical approach
If delivery speed matters, prefer stacks with strong conventions and abundant examples. Revisit choices as models evolve.
Multimodal Inputs: Screenshots and Voice
Screenshots for clarity
Paste UI screenshots to highlight visual bugs or share design inspiration. The model can compare intended and actual states directly.
Voice for speed
Voice tools like Aqua can stream spoken instructions into agents, often doubling usable input speed. Minor transcription errors rarely matter to modern models.
Refactor Frequently (With Tests in Place)
Refactor after green tests
Once features pass tests, refactor aggressively. Ask the LLM to identify duplication and suggest extractions.
Keep files small, functions focused, and modules cohesive. This reduces future prompt length and misunderstanding.
Keep Experimenting
The landscape changes weekly
Different releases outperform others on planning, debugging, or implementation. Regularly re-evaluate which models fit which tasks.
Maintain a simple benchmark routine: small planning task, a medium refactor, a gnarly bug fix, and a fresh feature stub. Track which model wins each category.
Founders’ Field Tips (Condensed)
Break out of loops: If the IDE agent stalls, paste the same question into the LLM’s web UI. Often yields a fresh path.
Parallelize models: Run Cursor and Windsurf on the same task with identical context. Compare results and choose the best.
Think of prompts as code: Provide full context and precise constraints. Treat language as the programming medium.
Start from tests: Handwrite test cases first to set hard boundaries. Let the model generate code to satisfy them.
Architect up front: Spend dedicated time in a plain LLM to design scope and architecture before letting an agent free-run inside the repo.
Watch for rabbit holes: If the model keeps regenerating or you’re copy-pasting error messages endlessly, pause and diagnose missing context or a wrong approach.
Use version control hard resets: Don’t layer prompts on a broken base. When a correct fix appears, reset and re-apply cleanly.
Prefer high-level tests: E2E and integration tests catch unintentional side effects.
Leverage AI beyond code: Delegate DevOps chores (DNS, hosting CLIs) and micro-assets (favicons) to models.
Switch models when stuck: Different models succeed on different problems. Rotate and retry.
Write long instruction files: Project-specific rules boost agent effectiveness dramatically.
Keep docs local: Download API docs for reliable grounding.
Prototype complex features separately: Build a minimal reference first; then integrate.
Choose conventional stacks: Mature frameworks with consistent patterns give better model outputs.
Add multimodal context: Use screenshots for UI issues; use voice input to move faster.
Refactor with tests: Clean structure improves future model interactions.
Stay curious: Re-test models often. Winners shift quickly.
A Step-by-Step Workflow You Can Reuse
Tooling setup
Choose a primary agent (Cursor, Windsurf, Claude Code) and a backup. Initialize a clean Git repo.
Project plan
Co-author PLAN.md with the LLM: requirements, architecture, sections, “won’t do,” and ideas later.
Instructions + docs
Add a rules file with conventions and constraints. Download API docs into /docs and tell the agent to read locally first.
Tests first
Handcraft high-level integration tests for the first section.
Implement one section
Ask the LLM to implement only that section. Run tests. Fix or roll back. Commit when green.
Parallel drafts (optional)
Submit the same prompt to a second model. Compare outputs; choose the better one.
Debug with discipline
Paste exact errors. For complex issues, list hypotheses, test one at a time, and reset between attempts. Add logging if needed.
Refactor
After green tests, ask the LLM to propose refactors. Keep files small and interfaces clean. Commit.
Rinse and repeat
Mark the section complete in PLAN.md. Move to the next. Keep the repo clean with frequent commits.
When stuck
Switch models or paste into a plain LLM chat. Prototype thorny features in a separate mini-repo. Integrate once stable.
Ship
Use AI to handle deployment checklists, DNS, or CI tweaks. Keep documentation and tests updated.
Prompts and Patterns That Work
Context-rich task prompt
You are helping implement Section 2 from PLAN.md.
- Language: TypeScript
- Framework: Next.js (App Router)
- Constraints: Accessibility-first, server actions for mutations, Prisma for DB
- Docs: Read /docs/payments-api/* before coding
Task: Build checkout flow per PLAN.md §2.1–2.3 only.
Deliverables: routes, server actions, schema updates + migration, page, tests (Playwright)
Stop after tests pass locally. Do not modify unrelated modules.
Bug triage prompt
We hit this error. Propose 3–4 plausible root causes before any code changes.
Error:
[stack trace here]
Repo constraints:
- Payments must be idempotent
- All mutations via server actions
- DB: Postgres via Prisma
Return: ranked hypotheses, observability steps (logging or tracing), and minimal code diffs for the top hypothesis.
Refactor prompt
Identify duplication and tight coupling in /app/(checkout) and /lib/payments/*.
Propose refactors that preserve the external API.
Output: a stepwise plan + code diffs. Run existing Playwright tests to confirm no regressions.
Instruction file seeds (rules)
Coding style, folder layout, naming conventions
Error handling and logging policy
Testing levels and coverage expectations
“Never modify” paths and third-party code boundaries
“Read local docs first” directive
Key Takeaways
Treat vibe coding as disciplined collaboration with AI, not a shortcut around engineering fundamentals.
Plan first, build in small increments, and keep a clean Git history.
Rely on high-level tests to guard behavior and catch accidental side effects.
Reset often to avoid layered, unclear changes. When stuck, switch models or surfaces.
Strengthen agents with detailed rules and local documentation.
Prototype complex features in isolation, then integrate with stable interfaces.
Favor conventional, well-documented stacks to improve model outputs.
Use screenshots and voice to speed up iteration.
Refactor confidently once tests are green.
Keep experimenting. The best model for a task changes over time.
Vibe coding rewards clear thinking, strong boundaries, and short feedback loops. With a structured process and the practices above, teams can ship faster, debug smarter, and steadily improve AI-assisted development outcomes.
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