#011: 8 VIBE CODING TECHNIQUES That Will Transform Your Software Development Forever

Vibe coding is the hottest trend in AI coding today. Unfortunately most developers don't know how to get desired results with it. This post will shed light on what to do to make it work for you.

In today's high-velocity development landscape, even seasoned engineering teams face mounting technical debt and missed delivery targets. The complexity of modern software architecture continues to outpace traditional development methodologies. But there's a transformative shift occurring: AI-assisted development isn't replacing your engineering talent—it's creating exponentially more effective technical teams by augmenting human expertise with computational intelligence.

After conducting extensive R&D on AI-assisted development workflows across multiple enterprise projects, the data is conclusive. Teams implementing these techniques are achieving measurable improvements: 300-500% acceleration in development velocity with corresponding increases in code quality metrics and test coverage. Most significantly, the productivity multiplier scales with developer seniority—the more domain expertise your engineers possess, the more dramatic the efficiency gains. This is because communicating domain knowledge to the model gets you exponentially better model output.

The following techniques represent the critical difference between teams merely experimenting with AI and those systematically leveraging it as a competitive advantage. These aren't incremental improvements to existing workflows—they're fundamental architectural patterns for human-AI collaboration that will redefine your engineering organization's capabilities.

1. Master the Art of Context Management

The #1 reason AI-assisted coding fails on larger projects? Context collapse.

As highlighted by experienced Cursor users, AI models struggle when they lose the big picture. The breakthrough solution? Strategic session management.

Instead of one marathon session, break your AI interactions into focused micro-sessions:

Workspace
├── Feature A (Session 1)
│   ├── Task 1.1
│   └── Task 1.2
├── Feature B (Session 2)
│   ├── Task 2.1
│   └── Task 2.2
└── Integration (Session 3)

Each time you start a new session:

1. Use a consistent "boilerplate" prompt that sets expectations. You don't even have to paste it, you can store it in a markdown file and simply reference it in your context (if your agent supports this)

2. Explain the current part of the system you're working on

3. Include a reference to your project's core goals

When your session gets too long and responses deteriorate, don't fight it—start fresh! Each session should culminate in a fully tested feature. Keep the features small, commit them to git, and then start a new session for the next feature. This pattern alone can eliminate 80% of frustrating AI hallucinations and circular changes while ensuring your codebase remains stable and well-documented through incremental commits.

2. Exploit the Power of Digital Notepads

Here's a technique that fewer than 10% of AI-assisted developers use, but it's a huge productivity boost: permanently pinned notepads.

Create a dedicated notepad file with:

• Your project's core architectural decisions

• System boundaries and component responsibilities

• Current implementation priorities

• Known issues to avoid

• Shell tools to use - for example glab or gh

Keep this notepad permanently stapled to your agent context by including it in every new session. This gives your AI assistant a consistent reference point that won't drift, even as your conversation evolves.

# PROJECT NOTEPAD
## Core Architecture
- RESTful API with GraphQL overlay
- Next.js frontend with Redux state management
- PostgreSQL with schemas under `src/db` for persistent storage

## Current Goals
- Implement user authentication flow
- Optimize query performance on dashboard
- Fix pagination bug on search results

## Implementation Notes
- All new components should use functional style with hooks
- CSS-in-JS approach with styled-components
- Follow repository pattern for data access

This simple technique creates an "anchor point" for your AI assistant, drastically reducing guesswork on the part of the assistant and keeping development focused on your actual priorities.

3. Flip the Script on Test-Driven Development

If you're still writing your own unit tests, you're wasting precious developer time. The AI revolution has completely inverted the traditional TDD workflow in a way that dramatically improves project quality.

Here's the approach:

1. Design your system architecture collaboratively with AI

2. Have the AI write comprehensive tests first

3. Review these tests to make sure they make sense

4. Use those tests as a specification framework to guide implementation

5. When implementation fails, have the AI fix it so that tests pass

This isn't just about saving time—it's about creating a self-reinforcing development loop where each iteration improves both the quality of your tests and your implementation.

The magic happens because tests serve as an unambiguous, machine-readable specification that keeps the AI focused on your actual requirements. When the AI starts to hallucinate or lose context, failing tests provide concrete evidence that steers development back on track.

4. Apply the "Locality of Behavior" Principle

One of the most powerful concepts for development comes from the HTMX project's "Locality of Behavior" principle:

"The behavior of a unit of code should be as obvious as possible by looking only at that unit of code."

This principle becomes exponentially more important when working with AI assistants. Compare these two approaches:

Poor locality example (jQuery):

// In main.js
$("#submitButton").on("click", function(){
  $.ajax({
    url: "/api/submit",
    // more config...
  });
});

// In HTML file
<button id="submitButton">Submit</button>

Good locality example (HTMX):

<button hx-post="/api/submit" hx-target="#results">
  Submit
</button>

The second approach makes the behavior obvious just by looking at the element itself. This clarity is crucial because AI assistants are much more effective when they can understand behavior from localized context rather than having to track connections across multiple files. This approach also reduces costs when working with AI assistants, as they only need to modify a single file instead of coordinating changes across multiple files, which requires more context and tokens to process.

When designing your codebase for AI assistance (and developer sanity):

1. Favor declarative patterns over imperative ones

2. Keep related code physically close together

3. Use explicit interfaces between components

4. Consider colocation over strict separation of concerns

5. Demand Explicit Planning from Your AI

Here's one of the most powerful techniques I use: forcing the AI assistant to explain its plan before writing code dramatically improves code quality.

Create this work cycle in every AI session:

1. Explain what you want to achieve

2. Ask the AI to analyze the repository based on your request and outline a plan

3. Save this plan to reference later

4. For each implementation step, have the AI reference the plan when coding

5. After each implementation, ask for a recap and next steps

This structured approach gives AI assistant the much needed input context that keeps it focused and aligned on the solution before diving into code, reducing the chance of half-baked implementations. It also creates checkpoints where you can course-correct before the AI goes too far down an unproductive path.

Me: Analyze this repository and create a detailed plan for implementing user
authentication with JWT tokens. Do not implement any code yet, just write the plan and save it into features/auth-with-jwt-tokens.md.

AI: Ok I will create a plan for you.

> Listing directory...
> Reading file...
...

Generating...

@features/auth-with-jwt-tokens.md

This planning-focused approach prevents the common pitfall where AI assistance turns into a disjointed series of small fixes without coherent architecture.

6. Embrace Modular Design with Clear Component Boundaries

Complex projects require modular design with explicit boundaries, especially when using AI assistance. This isn't just good software design—it's essential for effective AI acceleration.

Create clear component boundaries by:

1. Defining explicit and well documented interfaces between modules

2. Using dependency injection rather than tight coupling

3. Building "holographic" components where behavior is self-contained

4. Maintaining a registry of reusable patterns

When AI assistance has access to well-defined interfaces, it can focus on implementation details without needing to understand the entire system at once. This dramatically improves the quality of AI suggestions while also reducing the mental load on human developers.

Consider this pattern for organizing your project:

src/
├── features/
│   ├── auth/
│   │   ├── index.ts          # Public interface only
│   │   ├── service.ts        # Implementation details
│   │   └── types.ts          # Shared types
│   └── products/
│       ├── index.ts
│       ├── service.ts
│       └── types.ts
└── shared/
    ├── api/
    ├── hooks/
    └── utils/

With this structure, you can focus AI sessions on specific features while maintaining clean integration points. Each component becomes a self-contained unit that can be reasoned about independently.

7. Git Like Your Career Depends On It (Because It Does)

The final game-changing practice: obsessive version control. When working with AI, version control isn't just for collaboration—it's your safety net and the most powerful code review tool for ai code.

Transform your Git workflow with these AI-specific practices:

1. Commit frequently each time a generation step is finished with working code as a result.

2. Use descriptive commit messages that document generation process so that these can then be used to create a descriptive merge request message.

3. Use trunk based development with feature branches and squashed MRs (this means you can commit a lot in the branch but the final merge to main will be a single squashed commit with your MR description as git message)

4. Don't wait for "perfect" code before committing because sometimes assistants can break code and you want to be sure that you can always go back to working solution.

5. Learn to use Git's time-travel capabilities (revert, reset, checkout)

When AI assistance goes wrong (and it will), having granular commits means you can quickly revert to a known-good state without losing good implementations.

# Example Git workflow
git checkout -b feature/user-authentication origin/main
# Make initial changes with AI assistance
git add .
git commit -m "Add user authentication initial implementation"

# Review your changes with tig (a text-based git repository browser)
# If you don't have tig installed, equivalent commands:
git log --oneline --graph --decorate  # View commit history
git show HEAD  # View the latest commit details

# Continue developing with more AI help
git add .
git commit -m "Refine authentication flow with error handling"

# Review progress again
tig  # Text interface git browser
# or equivalent:
git log --oneline --graph --decorate
git diff HEAD~1 HEAD  # Compare current commit with previous

# If AI assistance creates bugs, you have options:
# Option 1: Using gitk (graphical git browser)
gitk  # Launch the GUI, right click on commit and select "Reset branch to here" then "Hard"

# Option 2: Equivalent raw git commands
git log --oneline  # Find the commit hash to revert to
git reset --hard <commit-hash>  # Hard reset to that commit
# Or to just revert to the previous commit:
git reset --hard HEAD~1

Remember: Git commits are cheap, but your time is incredibly valuable. Don't wait for perfection—commit early and often.

8. Leverage Configuration Files to Control Your AI Assistant

Here's the advanced technique that many Cursor users find valuable but is often overlooked: strategic use of configuration files.

The two most powerful configuration files you should add to your project immediately:

The Power of .cursorrules Files

A .cursorrules file placed in your project's root directory acts as a permanent system prompt for Cursor AI. It defines custom rules and provides context that guide how the AI behaves when generating code for your project.

Unlike the simple examples shown earlier, proper .cursorrules files follow specific formatting conventions and can be highly customized for different technology stacks and development needs.

Here's an example of a well-structured .cursorrules file for a C++ project:

--- 
description: C++ Programming Guidelines
globs: **/*.c,**/*.cpp,**/*.h,**/*.hpp,**/*.cxx,CMakeLists.txt,*.cmake,conanfile.txt,Makefile,**/*.cc
alwaysApply: false 
--- 

# C++ Programming Guidelines

## Basic Principles
- Use English for all code and documentation.
- Always declare the type of each variable and function (parameters and return value).
- Create necessary types and classes.
- Use Doxygen style comments to document public classes and methods.
- Follow the one-definition rule (ODR).

## Nomenclature
- Use PascalCase for classes and structures.
- Use camelCase for variables, functions, and methods.
- Use ALL_CAPS for constants and macros.
- Use snake_case for file and directory names.
- Avoid magic numbers and define constants.
- Start each function with a verb.

## Project Structure
- src/ for source code
- include/ for header files
- test/ for tests
- third_party/ for external libraries
- doc/ for documentation
- Use CMake or similar build system.
- Separate interface (.h) from implementation (.cpp).
- Use namespaces to organize code logically.

## Standard Library
- Use the C++ Standard Library whenever possible.
- Prefer std::string over C-style strings.
- Use std::vector, std::map, std::unordered_map, etc. for collections.
- Use std::filesystem for file operations.
- Use std::chrono for time-related operations.

For a Next.js project, you might use something like this:

---
description: Next.js Development Guidelines
globs: **/*.js,**/*.jsx,**/*.ts,**/*.tsx,**/*.css,**/*.scss,next.config.js
alwaysApply: true
---

# Next.js Development Guidelines

## Core Principles
- Use the App Router architecture
- Create page components using React server components when possible
- Implement client-side interactivity with 'use client' directives only when needed
- Follow atomic design principles for UI components
- Use proper SEO practices in all public-facing pages

## Code Style
- Use TypeScript for type safety
- Create interfaces for all data models and API responses
- Implement functional components with hooks
- Use proper error boundaries
- Implement proper loading states and fallbacks

## Project Structure
- /app - App router pages and layouts
- /components - Reusable UI components
- /lib - Utility functions and shared logic
- /hooks - Custom React hooks
- /styles - Global styles and themes
- /public - Static assets

## Data Fetching
- Use React Server Components for data fetching when possible
- Implement proper caching strategies
- Handle errors gracefully with try/catch
- Use Next.js data fetching methods appropriate for the component type

The .cursorrules file can include several key sections:

1. YAML Front Matter - Contains metadata about the rules

   • description: Brief description of the rules

   • globs: File patterns these rules apply to

   • alwaysApply: Whether to apply these rules to all matching files

2. Core Principles - Fundamental architectural and design principles

3. Code Style - Specific coding conventions and best practices

4. Project Structure - How the codebase should be organized

5. Framework-Specific Guidelines - Rules particular to your tech stack

What makes .cursorrules so powerful is that it provides consistent baseline context for the AI assistant without consuming your valuable conversation context window. It encodes your project's standards, architectural decisions, and best practices, ensuring that the AI's suggestions align with your established conventions.

You can find hundreds of example .cursorrules files in the awesome-cursorrules repository, covering virtually every major programming language, framework, and development scenario—from React and TypeScript to Python, Go, and specialized domains like blockchain development.

The Power of Cursor Ignore Files

As of Cursor version 0.46, there are two powerful configuration files that control how the AI assistant interacts with your codebase:

1. .cursorignore: Makes a best-effort attempt to exclude files from both AI features and indexing

2. .cursorindexingignore: Controls only which files are indexed for search and context features

The Critical .cursorignore File

The .cursorignore file is your primary tool for controlling AI access to your code. It attempts to exclude files from:

• Tab and chat requests

• Context for AI features

• Indexing for search and context

• @-symbols and other context tools

This is particularly valuable for:

# Sensitive configuration files
.env*
secrets.json

# Proprietary code you don't want analyzed
src/proprietary/
lib/licensed/

# Files containing secrets
config/credentials.yml

Important: The .cursorignore works on a best-effort basis and isn't guaranteed to block 100% of file access in all scenarios.

The Supplementary .cursorindexingignore File

The .cursorindexingignore file only affects indexing. Files listed here:

• Won't be automatically indexed for search and context

• Can still be manually included in context

• Can still be accessed by AI features if specifically requested

This file automatically inherits all patterns from your .gitignore files and is perfect for:

# Generated files (speeds up indexing)
dist/
build/
node_modules/

# Large data or binary files
assets/images/
public/videos/
*.pdf
*.jpg

# Third-party code
vendor/

Both files use the same syntax as .gitignore, supporting patterns like directory exclusions, file type filtering, and negation patterns with !.

By strategically configuring these two files, you create a structured environment that significantly improves the quality and security of AI-generated code by providing consistent guidance without consuming your valuable context window. As one developer noted, "it's the difference between a chaotic assistant and a disciplined coding partner."

Recap: Revolutionize Your Development Workflow

We've explored eight powerful techniques that will transform how you leverage AI in your software development process:

1. Master Context Management - Break work into focused micro-sessions to prevent context collapse

2. Use Digital Notepads - Maintain a permanent reference point for project architecture and priorities

3. Flip the Test-Driven Development Script - Let AI write comprehensive tests as specifications

4. Apply "Locality of Behavior" - Keep related code close together for better AI understanding

5. Demand Explicit Planning - Force AI to outline its approach before writing any code

6. Embrace Modular Design - Create clear component boundaries with explicit interfaces

7. Git Like Your Career Depends On It - Use obsessive version control as your safety net

8. Leverage Configuration Files - Control your AI assistant with strategic configuration

These aren't just incremental improvements—they represent a fundamental shift in how software development works in the AI age. By using these techniques more often, you'll dramatically boost your productivity and code quality while positioning yourself at the forefront of this technological revolution.

Continue Your AI Development Journey

Want to keep up to date with latest news on ai and software development? Connect with me on these platforms for ongoing insights, tutorials, and community discussions:

• LinkedIn: Follow me on LinkedIn for professional insights

• Discord: Join our developer community to discuss AI coding techniques with others

Remember: The AI revolution in software development isn't coming—it's already in full swing. By learning how to use agentic AI tools to boost productivity you can save a lot of valuable time and focus on higher level tasks instead.