Painless Vibe-Coding: A Complete Practical Guide from Real-Life Experience

Vibe coding is not "magic in a vacuum," but a conscious technique of rapid development with a reliable framework. Over the past months, I have journeyed from bolt.new to Copilot, Claude, Cursor, and Google AI Studio: more than a thousand prompts, dozens of iterations, and many lessons. Here is not a collection of clichés, but a refined set of principles, tools, and templates that truly save time, money, and nerves as the amount of code grows.

Introduction: How to Avoid Burnout

The idea is simple: move in small but precise steps, commit the results in Git, ask AI to make local "diff-only" patches rather than rewriting everything around, and maintain the design system from the first lines. Instead of "an agent that does everything for you," think of it as a collaborative work with an assistant to whom you clearly formulate the task and boundaries. This way, we avoid "hallucinations," unnecessary restructuring, and maintain control over the tokens.

1. Feature Plan and Specification (Inputs/Outputs/Errors)

Before any code, create a short but specific plan:

• 1–2 sentences about what we are doing: "The user does X and gets Y" (example: "Creates and shares task lists in 30 seconds without registration"). - 3–7 main flows (scenarios) and screens: for example, "Sign in -> Dashboard -> Create item." Without detailed markup — just names and order.
• Framework within files: "routes -> modules -> reusable components."
• Blacklist of "do not reinvent": buttons, inputs, alerts, helpers, validation schemes.

Next — feature specification: inputs, outputs, errors, limitations, done criteria. All subsequent prompts — strictly within this specification.

2. Plan UI/UX Ahead (Before Coding)

Before coding, visualize and experiment with the layout. Utilize tools like v0 to quickly see how screens will look and make adjustments accordingly.

Consistency is Key. Define a design system at the start and adhere to it:

• One base file: breakpoints, spacing scale, grid, typography.
• Create reusable components immediately: Button, Input, Select, Alert, Loader, Empty/Error states, or refer to a library. - Check after each iteration: Ensure that dimensions and fonts haven't "spread."
• In prompts: "Use existing ButtonX, InputY. Do not introduce new styles unnecessarily."

This will save you tons of time and prevent chaotic refactoring later, and it won't burn tokens.

Resources: 21st.dev — ready-to-use UI patterns with AI prompts, just copy-paste.

3. Popular Stack that Helps (Doesn't Hinder)

The broader the community and documentation, the more accurately AI hits the API and patterns. ## Practical Combinations:

• Next.js - Frontend + lightweight API layer.
• Tailwind CSS - Fast, consistent styles without "CSS soup" at the start.
• Fastify + MongoDB or Supabase - Minimal rituals, many ready-made recipes.

The key is not exoticism, but reliable model responses.

4. Git Discipline: Commit as Frequently as Possible

Your best insurance policy:

• One feature - separate branch.
• Small working chunks - frequent commits with clear messages. - Before making significant changes or reorganizations, commit or branch.

This way, you won’t have to "roll back" 700 lines if the AI "optimized" in the wrong place.

5. Breaking Down Complexity: Specification -> Skeleton -> Logic -> Tests

Do not give large prompts like "make the entire module complete." The AI will start hallucinating and output garbage. Divide any complex feature into stages (phases): instead of one big request - break it into 3–5 smaller ones or more if needed.

Sequential pipeline:

1. Routes and file skeletons ## 6. Specific Prompts with Boundaries (diff-only)

Garbage in, garbage out. Providing poor prompts results in receiving poor output. Prompts should be as detailed as possible — leave no room for AI guesswork. If it doesn't work out, launch Gemini 2.5 Pro in Google AI Studio and ask it to create a detailed prompt based on your idea. (Chat GPT also performs this task well.)

The final formulation is your control lever. Minimal template:

"Refactor ProfileForm.tsx: extract validation into a separate hook, do not touch the API, preserve prop names and the public API, use InputX and ButtonY." ## Do not change anything outside of the specified.

Add an example of a code/interface snippet — the model will "catch" the idea faster. The protective phrase "Do not change anything that was not requested" is mandatory.

7. Chat Context and Reset: When to Open a New Thread (Chat)

A large chat = drift of patterns, loss of context. Restarting is not a defeat; it’s hygiene:

• A brief introduction for the new window: "Feature X, files A/B/C, only touch what is allowed..."
• Remember: too much context is just as harmful as too little—keep only the essential.

8. Token Economy: Be careful with the budget

• Small patches instead of massive "rewrites."
• Diff-first: "show patch plan + diff, then apply."
• Model switching: simple tasks - Auto/smaller model; reviews and security - Gemini 2.5 Pro/Sonnet.

9. ## Code Hygiene: ESLint, Types, Minimal Tests

• Small, clean functions without "lazy" side effects.
• ESLint + autofixes; remove dead code at each step.
• Minimum unit tests: "happy path" + edge cases (null/empty/unknown type).
• TypeScript with strict rules - fewer comments, more guarantees.

Before committing, remove excessive logs and "temporary" comments - they distract attention during iterations and consume tokens.

10. # Security: A Short but Serious Checklist

(Actually just the obvious basics, but when working with AI, you always need to keep an eye on them)

There will always be flaws (in everyone), but these rules will save you from the toughest:

Trusting client data: accepting form/URL inputs directly. -> Fix: Always validate and sanitize on the server; escape outputs.

Secrets in frontend: API keys/credentials in React/Next.js client code. Fix: Keep secrets only on the server (environment variables, .env in .gitignore).

Weak authorization: Checking only "if logged in" instead of "if allowed to do this." Fix: The server should verify permissions for each action and resource.

Excessive error details: Showing stack trace/database errors to the user. Fix: General message to the user, detailed logs for the developer.

IDOR and ownership: Allowing user X to edit user Y's data through ID. ## 11. Issue Fixes for Server and Database Security

Ownership Verification: Ensure the server confirms that the current user owns this ID.

-> Fix: The server must verify that the current user owns this ID.

Ignoring DB Security: Bypassing measures such as RLS (Row-Level Security).

-> Fix: Define data access rules directly in the database (RLS).

Unsecured API: Lack of rate limits; unencrypted sensitive data.

-> Fix: Implement rate limiting through middleware; encrypt data at rest; always use HTTPS. Iterative Review (Gemini/Claude): problem -> risk -> patch

This is not easy to use because copying all files can be challenging, but even a simple analysis from a chat can reveal significant shortcomings, so we do it periodically.

After building the feature, copy all the code into Gemini 2.5 Pro (in Google AI Studio) or simply run large models for code and feature review — they have a large contextual window and excel at finding vulnerabilities and poor patterns.

How to work:

1. Tell Gemini: "You are a security expert. Find vulnerabilities." / "You are an expert in [your stack]. Identify performance issues and bad patterns."
2. Gemini returns a list of issues. Copy them into Claude in Cursor and instruct it to fix them.
3. After fixing, consult Gemini again until it confirms "everything is OK."

Goals of the analysis: security, performance, duplications, unnecessary dependencies.

Response format that works:

1. Issue;
2. Why it is an issue;
3. Risk;
4. Specific patch/steps. ## 12. "Stubborn" Bugs: Fixing Without Panic

If, after 2-3 attempts, things are still "off track":

1. Ask the model to list the top suspects in the dependency chain.
2. Add logs to the narrow spots, including facts (stack, payload, boundaries).
3. If necessary, roll back to the last successful commit and take small steps forward. Sometimes, this is the only way to solve problems. Or you can fix it manually, but that's not why we came to vibe coding. ## 13. Boundary Rule: "Do Not Alter Unless Asked"

AI loves to "tidy up" when it sees an opportunity. Constraints - the concluding phrase in every prompt: "Do not change anything beyond the listed items." After several iterations, the model begins to respect boundaries.

14. Repo Instructions and "Common AI Mistakes" as Code

• Maintain ai_common_mistakes.md: AI loves to move validation to the UI, change prop names, and remove necessary imports. Record everything here. - Folder instructions/ with Markdown examples, prompt templates, "Cursor Rules," and short best practices.
• In a new feature, add a link to the file of mistakes - it saves tokens and time.

15. Tools and Patterns That Speed Up Work Without Fuss

Tools:

• Storybook - isolated UI and pattern documentation.
• Playwright / Vitest - fast E2E/unit tests; pair well with diff patches.
• CodeSandbox / StackBlitz - instant sandboxes for PoC. - Sourcegraph Cody: deep search and contextual patches.
• Continue / Aider / Windsurf / Codeium: lightweight assistants for "getting stuck."
• Tabby / local LLM: cost-effective generation for template code.
• Perplexity / Phind: quick technical research and approach comparison.
• MCP (Model Context Protocol): standardized access to files/commands without "chatter" in the prompt.
• Commit message generators (also see CommitGPT): save time, but read before pushing. - ESLint with autofix - machine hygiene "at the entrance."

Working Patterns:

• Triple Pass Review: structure -> logic/data flow -> edge cases/security.
• Interface Freeze: freeze the public API/props before deep generations (refactoring).
• Context Ledger: short notes on features (files, decisions, outstanding TODOs) - easy to transfer to a new chat.
• Session Reset Cadence: regular reset of long sessions.
• Red Team Self-Check: separate pass for injections, IDOR, races.

16. ### Cursor Rules, Instructions, and "Not Being Afraid to Go Back"

• Cursor Rules: an excellent starting point; fix the stack, patterns, prohibitions, and anti-patterns.
• Instruction Folder: examples of components, short recipes for typical tasks (a replacement for the chat's "hot memory").
• If the model is off track, go back a step, clarify the prompt and context—continuing "by inertia" is costlier.

17. ## Prevention of Unwanted Changes by AI — Briefly

We reiterate the rule from point 13: "Do not add, remove, or rename anything that was not requested." Clear boundaries are better than emotional phrases, and they work more consistently.

18. Mini Checklist Before Commit

• The function fits into existing patterns and components.
• Types are strict without any; basic tests and logs are present.
• Security: secrets are on the server; permissions and validation are checked.
• UI is consistent: spacing, states, names.
• The commit message is short and informative. ## Useful External Resources

Below are some external, public sources (services and tools) that provide practical value when using the "vibe" approach.

bolt.new
What: An online environment for quickly creating a skeleton (Full-stack/frontend) with initial code generation through AI.
Why: Instant MVP/prototype before investing in the structure of the main repository.
When: In the phase of idea validation or finding a general form without detailed architecture. GitHub Copilot
What: An auto-completion and inline suggestion tool for editors (VS Code, JetBrains).
Why: To accelerate template code (configurations, helper functions, small React components) and reduce the amount of mechanical typing.
When: When you need a quick "sketch" or line completion rather than deep multi-file logic generation. (But now, it works more or less well here.)

Claude Sonnet / Anthropic Models
What: Models with strong contextual understanding and relatively accurate output.
Why: Review large chunks of code, provide suggestions on structure, security, and style.
When: Before refactoring, for identifying duplicates or potential vulnerabilities.

Cursor
What: IDE (a fork of VS Code) with deep LLM integration (chat + patch generation + "Rules"). ### Why:

Managed prompts, local diff patches, quick iterations without manual copying.

When:

In main development, when many small structural changes are needed.

cursor.directory

What:

A catalog of ready-made Cursor Rules and prompt templates.

Why:

A starting set of rules (style, architecture, protective constraints) instead of manual crafting.

When:

At the stage of setting up "rules of the game" in the project. Google AI Studio (Gemini 2.5 Pro)
What: An interface for models with a large context window.
Why: Comprehensive checks - security, performance, duplicates, dependencies; summarization before restructuring.
When: When the codebase is already significant and requires a "strategic overview."

21st.dev
What: A collection of UI patterns with example prompts.
Why: To unify the styles and structure of components without inventing "from scratch." When: before scaling the frontend or standardizing forms/lists.

Sourcegraph Cody
What: intelligent search across large repositories and patch generation.
Why: find all function usages and dependencies, construct a map of connections before making changes.
When: before deep refactoring or module removal.

CodeSandbox
What: cloud sandboxes for instant application launch without local installation. Why: To verify a library, test an idea, or demonstrate a concept to colleagues.
When: Early validation or isolated demonstration.

Storybook
What: An isolated environment for viewing and testing UI components.
Why: To ensure design system consistency and visual control of states (loading/empty/error).
When: During the extraction of common components or before frontend scaling. Playwright
What: A tool for E2E testing (browser scenarios with high accuracy).
Why: To verify key user flows after automatic patches by AI.
When: Before merging significant changes or releasing a version.

Vitest
What: A fast unit/integration tester for the Vite ecosystem/modern TS.
Why: To cover clean functions and hooks to stabilize refactoring. What: Tools for evaluating the quality of LLM integrations and prompts. Why: Objectively assess whether your rules/corrections are improving the outcome. When: When the number of prompts and models is greater than 1, and quality management is required. Why: Objectively assess whether the outcome improves from your rules/corrections.
When: When the number of prompts and models is greater than 1 and quality management is required.

Regex101
What: Online regular expression debugger with explanations.
Why: Quickly check and correctly build validation/parsing rules.
When: Before incorporating complex validation into a form/API.

How to effectively combine:

1. Idea → bolt.new (prototype) → transfer to Cursor.

2. Structure -> Sourcegraph (dependencies) -> Claude (notes) -> local patches.

3. UI -> 21st.dev (pattern) + Storybook (preview) + Tailwind (styles).

4. Security -> OWASP (checklist) + Gemini (audit) + Playwright (flows).

5. Cleanliness -> ESLint (rules) + TypeScript (strict types) + Vitest (tests).

Gradually introduce resources—not all at once: this reduces cognitive load and increases process stability.

Conclusion: Speed with a Framework

Vibe-coding is not chaos and not "an end in itself." This is a cycle of "vision -> small step -> verification -> stabilization." The better the vision and patterns, the more AI becomes a partner, not a source of surprises and problems. Mistakes will happen, but with this approach, you won't get lost—just keep iterating without difficulty.

Thank You for Your Attention

Thank you for reading. I hope the material is useful to you. If you liked it, please support our project and share it with colleagues.

P.S.: The image in the header is intentionally a bit quirky.