Vibe Coding Without Pain: A Complete Practical Guide from Real Experience
Vibe coding isn’t “magic in a vacuum,” but a conscious technique of rapid development with a reliable framework. Over the past months, I’ve transitioned from Bolt.New to Copilot, Claude, Cursor, and Google AI Studio: over a thousand prompts, dozens of iterations, and many lessons. Below is not a collection of platitudes but a polished set of principles, tools, and templates that truly save time, money, and nerves as the codebase grows.
Introduction: What is Vibe-Coding and How Not to Burn Out
The idea is simple: we move in small but precise steps, committing results to Git, asking AI to make local “diff-only” patches instead of rewriting the world, and maintaining a design system from the very first line…
Vibe Coding Without Pain: A Complete Practical Guide from Real Experience
Vibe coding isn’t “magic in a vacuum,” but a conscious technique of rapid development with a reliable framework. Over the past months, I’ve transitioned from Bolt.New to Copilot, Claude, Cursor, and Google AI Studio: over a thousand prompts, dozens of iterations, and many lessons. Below is not a collection of platitudes but a polished set of principles, tools, and templates that truly save time, money, and nerves as the codebase grows.
Introduction: What is Vibe-Coding and How Not to Burn Out
The idea is simple: we move in small but precise steps, committing results to Git, asking AI to make local “diff-only” patches instead of rewriting the world, and maintaining a design system from the very first lines. Instead of “an agent that does everything for you,” pair with a wise assistant to whom you clearly formulate the task and boundaries. This way, we avoid “hallucinations,” unnecessary restructuring, and do not lose control over tokens.
1) Planning and Mini-Contract: How Not to Get Lost in the First 30 Minutes
Before any coding, create a brief but concrete 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 to 7 main flows/screens - without excessive detail.
- Framework in terms of files: “routes -> modules -> reusable components.”
- Blacklist items: “do not reinvent the wheel” such as buttons, inputs, alerts, helpers, validation schemas.
Then, a feature mini-contract: inputs, outputs, errors, constraints, criteria for completion. Afterwards, all prompts are within the scope of this contract.
2) Design System and Consistency: The Framework for the Project
The most costly mistake is starting “on a vibe” without a grid and basic typography. To avoid turning refactoring into manual alignment of margins:
- One base file: breakpoints, spacing scale, grid, typography.
- Main components right away: Button, Input, Select, Alert, Loader, Empty/Error states.
- Check after each iteration: ensure sizes and fonts haven’t “spread out.”
- In prompts: “Use existing ButtonX, InputY.” New styles should not be introduced unnecessarily.
Resource for starting and inspiration: 21st.dev - ready-made UI patterns with prompts.
3) A Stack that Helps, Not Hinders
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-mess” at the start.
- Fastify + MongoDB or Supabase - minimal rituals, many ready-made recipes.
The main thing is not exotic, but reliable model answers.
4) Git Discipline: “Freeze Time” More Often
Your best insurance policy:
- One feature per branch.
- Small work chunks - frequent commits with clear messages.
- Before risky generations/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 the Complex: Spec -> Skeleton -> Flesh -> Harden
Not “do the whole module,” but a sequential assembly line:
1) Routes and file skeletons. 2. Basic markup and components.
- Logic, validation, states.
- Tests, security, optimization.
AI works more stably when the task boundaries are narrow and clear.
6. Prompts without “magic” and with protective boundaries
The final formulation is your control lever. The minimal template:
“Refactor ProfileForm.tsx: move validation to a separate hook, do not touch the API, preserve prop names and public API, use InputX and ButtonY.” The protective phrase “Do not change anything that was not requested” is mandatory.
7) Context and “Reset”: When to Open a New Chat
A large chat = drift of patterns, loss of context. Restarting is not a failure, but a form of hygiene:
- Brief introduction for a new window: “Feature X, files A/B/C, only allowed to touch …”
- Remember: too much context is as harmful as too little - leave only the essential.
8) Token Economy: Be cautious with the budget
- Small patches instead of giant “rewrites.”
- Diff-first: “Show the patch plan + diff, then apply.”
- Model switching: simple - Auto/smaller model; reviews and security - Gemini 2.5 Pro / Sonnet.
9) Engineering Hygiene: Tests, Logs, Types
- Small, clean functions without “lazy” side effects.
- Use ESLint with autofixes; remove dead code at every step.
- Minimum unit tests: “happy path” and edge cases (null/empty/unknown type).
- TypeScript with strict rules - fewer comments, more guarantees.
Before committing: remove unnecessary logs and “temporary” comments - they consume attention during iterations and use up tokens.
10) Security: A Brief but Serious Checklist
- Secrets and keys should only be stored on the server (use environment variables, .gitignore, and ensure they are not exposed to the client).
- Validate and sanitize inputs on the server, and ensure output is properly escaped.
- Permissions should not rely solely on login: each action and resource must undergo permission checks.
- Errors: provide a general message for the user and maintain a detailed log for the developer.
- IDOR and resource ownership: ensure the user has the right to access the specific ID. - DB rules (RLS and analogs) are better at the database level when appropriate.
- Rate limiting and encryption (HTTPS, data “at rest”).
11) Iterative Review with a “Big Head” (Google AI Studio / Gemini 2.5 Pro)
When deep inspection is needed:
- Brief overview: “The module does X for Y; main logic in functions...”
- Goals: security, performance, duplicates, unnecessary dependencies.
- Boundaries: “No global restructuring; suggest local patches/steps.”
Response Format that Works:
1) Issue; 2) Why it is an issue; 3) Risk; 4) Specific patch/steps.
Then proceed with local diff patches, small tests, and a review.
12) “Embedded” Errors: Procedure Without Panic
If, after 2-3 attempts, things are still “off”:
1) Ask the model to name the top suspects in the dependency chain. 2) Add logs to narrow spots, add facts (stack, payload, boundaries). 3) If necessary, rollback to a “green” commit and take small steps forward.
13) The Rule “Don’t Touch Unless Asked” - and Why I Always Repeat It
AI loves to “tidy up” when it sees an opportunity. Protection - the concluding phrase in each prompt: “Don’t change anything beyond the listed.” After several iterations, the model begins to respect boundaries.
14) “Common AI Mistakes” File and “Instructions” as Code
- Maintain
ai_common_mistakes.md: AI likes to move validation to UI, change prop names, and delete necessary imports - all of this goes here. - Folderinstructions/with markdown examples, prompt templates, “Cursor Rules,” and short best practices. - In the new feature, add a link to the file of mistakes - saves tokens and time.
15) Tools and Patterns That Accelerate Without Fuss
Tools:
- Storybook - isolated UI and documentation of patterns.
- Playwright / Vitest - fast E2E/unit tests; work 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: affordable 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 (see also CommitGPT): save time, but read before pushing. - ESLint with autofix: “entry-level” machine hygiene.
Work Patterns:
- Triple Pass Review: structure -> logic/data flow -> edge cases/security.
- Interface Freeze: lock the public API/props before deep generations.
- Context Ledger: short notes on features (files, solutions, outstanding TODOs) - easily transferable to a new chat.
- Session Reset Cadence: regular reset of long sessions.
- Red Team Self-Check: separate pass for injections, IDOR, races.
16) About Cursor Rules, Instructions, and “Not Being Afraid to Go Back”
- Cursor Rules: An excellent starting point; fix the stack, patterns, prohibitions, and anti-patterns.
- A folder with instructions: examples of components, short recipes for typical tasks (replacing the “hot memory” of the chat).
- If the model goes astray: go back one step, clarify the prompt and context—continuing “by inertia” is more costly.
17) Preventing Undesirable Changes from AI - Politely but Firmly
Be persistent in every request: “Do not add, delete, or rename anything that was not requested.” This significantly reduces the “optimization reflex.” Vulgar language is unnecessary - clear boundaries work better.
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.
- Commit message is short and informative.
Useful External Resources
Below are external, public sources (services and tools) that provide practical value in the “vibe” approach.
What: An online environment for quickly creating a framework (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 searching for a general form without detailed architecture.
What: A tool for autocomplete and inline suggestions in the editor (VS Code, JetBrains).
Why: To speed up template code (configurations, helper functions, small React components) and reduce the amount of manual typing.
When: When a quick “sketch” or line completion is needed, rather than deep multi-file logic generation. What: Models with strong contextual understanding and cautious outputs. Why: Review of large code blocks, suggestions on structure, security, and style. When: Before refactoring, to find duplications or potential vulnerabilities.
Cursor What: IDE (fork of VS Code) with deep integration of LLM (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.
What: A catalog of ready-made Cursor Rules and prompt templates.
Why: A starter set of rules (style, architecture, protective constraints) instead of manual development.
When: At the stage of setting up “rules of the game” in the project. Google AI Studio (Gemini 2.5 Pro)
What: An interface to models with a large context window.
Why: Comprehensive checks - security, performance, duplications, dependencies; summarization before restructuring.
When: When the codebase is already significant, and a “strategic overview” is needed.
What: A collection of UI patterns with example prompts.
Why: To unify styles and component structures without inventing “from scratch.” When: before scaling the frontend or standardizing forms/lists.
What: Intelligent search across large repositories and patch generation.
Why: To find all function usages/dependencies and build a picture of connections before making changes.
When: before deep refactoring or module removal.
What: Cloud sandboxes for instant application launch without local installation. Why: To test a library, evaluate an idea, or demonstrate a concept to a colleague.
When: Early validation or isolated demonstration.
What: An isolated environment for viewing and testing UI components.
Why: To ensure the consistency of the design system and visually check states (loading/empty/error).
When: During the extraction of common components or before scaling the front end. Playwright
What: A tool for E2E tests (browser scenarios with high accuracy).
Why: To check key user flows after automatic patches by AI.
When: Before merging important changes or releasing a version.
What: A fast unit/integration tester for the Vite ecosystem/modern TS.
Why: To cover pure functions and hooks to stabilize refactoring. When: Right after extracting small utilities or logic into separate modules.
What: LLM search engines with summarized answers and links.
Why: Instantly learn API nuances, library statuses, and compare approaches.
When: Before choosing a stack or optimization.
What: Tools for assessing the quality of LLM integrations and prompts.
Why: Objectively evaluate if the result improves with your rules or corrections. When: when the number of prompts and models is greater than 1, and quality management is required.
What: An online debugger for regular expressions with explanations.
Why: To quickly verify and correctly construct validation/parsing rules.
When: Before implementing complex validation in a form/API.
How to effectively combine:
- Idea -> bolt.new (prototype) -> transfer to Cursor.
- Structure -> Sourcegraph (dependencies) -> Claude (comments) -> local patches.
UI
- 21st.dev (pattern) + Storybook (review) + Tailwind (styles).
Security
- OWASP (checklist) + Gemini (audit) + Playwright (flows).
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 or an “end in itself.” It is a cycle of “vision -> small step -> check -> stabilization.” The better the vision and patterns, the more AI becomes a partner rather than a source of surprises and problems. Mistakes will occur, but with this set, you won’t get lost—just continue to iterate without pain.
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