Vibe Coding: A Guide to Modern AI-Assisted Development

Note: This area is changing fast, and we’ll keep updating this guide as new methods and recommendations come up.

Table of Contents

What is Vibe Coding?

If you’ve heard the term “vibe coding” and wondered what it means, you’re not alone. Simply put, vibe coding is a modern way of writing software where you use everyday language to give instructions, and AI tools help generate the code for you. It’s like having a conversation with a coding partner who understands plain English (or whatever language you prefer) and turns your ideas into actual code.

But how does it work? There are a few key parts that make vibe coding possible:

  • The Prompt: This is the set of instructions or context you provide. It’s what tells the AI what you want to build or fix. For example, you might say, “Create a simple login form that checks if a user’s password has at least 8 characters” – that’s a prompt.
  • The Client: This is the tool or app you use to talk to the AI. Think of it as the interface between you and the coding AI. Popular examples include Amazon Q Developer and Cline. These clients let you type in your prompts and get code back.
  • Extra Context: Sometimes, the AI needs more information to do a good job. This could be details about specific servers (like AWS MCP servers) or existing code in your project. Adding this extra context helps the AI understand your project better and generate more useful code.

It’s important to remember that vibe coding – and AI in general – is meant to help you work faster, not replace you. As a developer, you’re still in charge. You decide the big-picture stuff: how the project is structured, what features are most important, and whether the code works the way it should. The AI is like a helper that handles some of the tedious parts, but you’re the one who makes sure everything fits together and meets the project’s goals.

Warning: Never just copy and paste code from an AI without checking it first. Always:

  • Read through the code carefully to make sure you understand what it does
  • Check that all the tools and libraries it uses (called “dependencies”) are correct and safe
  • Run security checks to make sure there are no hidden problems
  • Test the code in a safe environment before using it in a real project

Choosing and Using AI Development Clients

AI development clients are the tools you use to do vibe coding. With so many options out there, it can be hard to pick the right one. Here’s what you need to know to choose and use them effectively.

How to Pick the Right Client

When choosing an AI development client, start by thinking about what your team or company needs. For example:

  • Rules and Security: Does your company have rules about which tools you can use? Some clients might not meet certain security or compliance standards, so that’s a good first check.
  • Cost and How It Works with Your Tools: Some clients are free, some cost money. Also, check if they work with the programs you already use (like your code editor). If a client fits seamlessly with your workflow, it will save you time.

Making the Most of Client Features

Each client has its own special features that can make your work easier. For example, Cline has a “Plan mode” where you can talk through your ideas and work out the details before you start coding. It’s a good idea to use this mode to hash out exactly what you want to build – once you’re clear, you can switch to “Act mode” to generate the code. This way, the code you get will match your plan.

Don’t forget to keep up with updates, too. Clients often add new features that can make your work faster. Check their websites or help docs every now and then to learn about what’s new.

Making Sure Features Work Together

Not all clients work with all tools. For example, if you want to use a specific server (like the CDK MCP server), you need to make sure your client can work with it. The MCP servers (which help the AI understand different coding tasks) have different features, so check the client’s documentation to see if it supports what you need.

Using More Than One Client

You don’t have to stick to just one client. Different clients are good at different things. For example, you might use Cline for building backend systems or working with AWS CDK, and Amazon Q CLI for fixing AWS permission issues or checking network problems. Mixing and matching can help you get more done.

Choosing MCP Servers

There are over 40 MCP servers available, and more are being added all the time. But you don’t need to learn all of them. Just focus on the ones that match what you’re working on. Read their documentation to see if they fit your project, and try them out to see how well they work.

Setting Up Requirements and Design Guidelines

Before you start typing prompts or writing code, there’s some important groundwork to do. Skipping these steps can lead to confusion, extra work, and code that doesn’t do what you need. Here’s what you need to do first:

Define What You Need to Build

Start by being clear about what your project is supposed to do. What problems does it solve? Who will use it? What features are must-haves, and which are nice-to-haves? Writing these down (maybe in a simple list or a document) helps everyone – including the AI – stay on the same page.

Set Design and Coding Rules

Every project needs guidelines. For example: “We’ll use Python for this project” or “All code must have comments explaining what it does.” These rules keep the code consistent, which makes it easier to read and fix later.

Note Any Limits or Restrictions

Are there things your project can’t do? Maybe it can’t use a certain tool because of security rules, or it has to work on older computers. Writing down these constraints helps the AI avoid generating code that won’t work.

Keep Track of All This Information

Put all your requirements, rules, and constraints in a markdown file (a simple text file with headings and lists) and keep it updated. This way, when you’re working with the AI, you can share this file to give it the full picture of your project.

Talk to the AI to Refine Your Plans

You can use the AI to help you work out your requirements and design. For example, you might ask, “What features should a simple to-do app have?” The AI can suggest ideas, and you can pick the ones that fit. But remember: you’re the boss. The AI’s suggestions are just ideas – you decide what’s best for your project.

Why is this step so important? Because the clearer your requirements, the better the code the AI will generate. It’s like giving someone directions: if you’re vague, they might get lost. If you’re specific, they’ll get to the right place faster.

But don’t let the AI write all your documents for you. You need to understand every part of your project. AI can sometimes agree with you even if you’re wrong, so it’s better to ask questions (“What could go wrong with this approach?”) instead of making statements (“This approach is perfect”). This way, you’ll get better feedback.

Mastering the Art of Prompting

If vibe coding is like having a conversation with a coding partner, then your prompts are what keep that conversation useful. A good prompt can save you time and get you great code; a bad one might leave you with something that doesn’t work. Here’s how to write better prompts.

Be Specific

The more details you give, the better the AI can help. Instead of saying, “Make a calculator,” try, “Make a simple calculator in JavaScript that can add, subtract, multiply, and divide. It should have buttons for each number (0-9) and each operation, and show the result in a box at the top.” The second prompt gives the AI clear instructions, so you’re more likely to get what you need.

Include Relevant Context

If you’re working on a project, share parts of your existing code or explain how the new code will fit in. For example: “I have a Python function that gets user data from a database. Can you write a function that checks if the user is over 18, using the ‘birthdate’ field from the user data?” Sharing the existing function (or a snippet of it) would make this even clearer.

Break Big Tasks into Small Ones

Trying to build a whole app with one prompt is hard – for you and the AI. Instead, break it down. For example, if you’re making a blog app, start with a prompt to create the database structure, then another to make the user login, then another for the post editor. Smaller tasks are easier to review and test, and the AI can focus better on each part.

Use Tools to Improve Your Prompts

If you’re not sure how to phrase a prompt, there are tools that can help. Amazon Bedrock Prompt Optimization, for example, can rewrite your prompts to make them clearer and more effective. It’s like having a proofreader for your instructions.

Try Metaprompting

Metaprompting is a fancy word for “talking through your idea first, then using that talk to make a prompt.” For example, you might start by saying, “I need to build a feature that lets users reset their passwords. Let’s think about how this should work. First, the user clicks ‘Forgot Password,’ then they get an email with a link, then they click the link and enter a new password…” After this discussion, you can summarize it into a clear prompt: “Build a password reset feature with these steps: 1. User clicks ‘Forgot Password’ and enters their email. 2. System sends an email with a 24-hour valid reset link. 3. User clicks the link and enters a new password. 4. System updates the password and sends a confirmation email.” This method helps you organize your thoughts and gives the AI a clear roadmap.

Testing and Validating Your Code

No matter how good your prompts are, you can’t be sure the code works until you test it. Testing is how you catch mistakes, make sure the code does what it’s supposed to, and keep your project reliable. Here’s how to do it right.

Test Small Changes as You Go

Don’t wait until you’ve written 1000 lines of code to test. Test each small part as you build it. For example, if you make a function that adds two numbers, test it with different numbers (positive, negative, zero) to make sure it works. This way, if something breaks, you’ll know exactly which part caused the problem.

Use Automated Tests When You Can

Automated tests are like little programs that check your code for you. Once you set them up, they can run every time you make a change, so you catch problems early. For example, you can write an automated test that checks if your login form rejects wrong passwords. The AI can help you write these tests, but you need to tell it what to check.

Make Sure It Matches Your Requirements

After testing, ask: “Does this code do what we needed it to do?” Go back to your requirements document and check each point. If your requirement was “Users should get an error message if they enter an invalid email,” test that scenario to make sure the error message appears.

Keep a Good Test Suite

A test suite is a collection of all your tests. It should cover all the important parts of your project: the main features, edge cases (like very large numbers or empty inputs), and even things that rarely happen. Keeping this suite up to date means you can check if new code breaks old features (this is called “regression testing”).

Run Regular Security and Quality Checks

Tools can scan your code for security problems (like weak passwords or open doors for hackers) and quality issues (like messy code that’s hard to fix). Run these scans regularly to keep your code safe and easy to work with.

Here’s an important note: AI can generate test code, but it’s not always good. Sometimes, the tests are too simple (like checking if 2+2=4 when your function is supposed to handle much more) or they don’t check the right things. That’s why it’s better for you to come up with the test ideas. You know the project’s goals, the edge cases, and what could go wrong. The AI can help write the test code once you tell it what to test, but you should always review it to make sure it’s thorough.

This approach is called “test-driven development” (TDD). With TDD, you write the tests first, then write the code to pass those tests. It might seem backwards, but it helps you think clearly about what the code needs to do before you start writing it.

Creating and Maintaining Documentation

Documentation is like a map for your project. It tells you (and anyone else working on the project) what the code does, how to use it, and why certain decisions were made. Good documentation saves time and prevents confusion, especially when working with AI.

Document Every Change

Whenever you add a new feature, fix a bug, or change how something works, write it down. For example: “Added a ‘remember me’ checkbox to the login form – saves user session for 30 days.” This helps you track what’s been done and why.

Make Sure the AI Helps with Documentation

Many AI clients can generate documentation along with code. For example, if you’re writing Python, the AI can add “docstrings” – little notes in the code that explain what a function does. It can also help write README files, which are like instruction manuals for your project. Just ask it to: “Add a docstring to this function explaining what it does, what inputs it takes, and what it returns.”

Keep Documentation Up-to-Date

Code changes over time, and documentation should too. If you update a function to work differently, update the docstring or README to match. Outdated documentation is worse than no documentation – it can lead to mistakes and confusion.

Working with AI to Co-Author Documentation

You can team up with AI to create and update documentation. For example, when planning a database, you can ask the AI to draw an ERD (a diagram showing how database tables connect). Then, if you change the database structure later, tell the AI to update both the diagram and the written documentation.

This works for other parts of the project too – like API designs (how different parts of the app talk to each other) or network setups. The key is to split your documentation into parts that make sense (like “Database Schema,” “API Endpoints”) and keep each part short and clear. This way, both you and the AI can stay on track.

Tip: Well-organized documentation helps you and the AI remember important details about the project, even as it grows. It’s like keeping a journal – it helps you pick up where you left off.

Understanding the Limitations

AI tools are powerful, but they’re not perfect. Knowing their limits helps you use them better and avoid frustration.

Managing MCP Servers and Tools

MCP servers and tools help the AI do its job, but using too many can slow things down. It’s like having too many apps open on your phone – everything gets laggy. Check your client’s documentation to see how many servers or tools it can handle well, and stick to that number.

Keeping Conversations Organized

If you have a long conversation with an AI client (typing back and forth for days or weeks), the client might slow down. That’s because it has to remember everything that was said before (this is called “context”), and too much context gets hard to handle.

To avoid this:

  • Start a new conversation for each new feature or task. For example, have one conversation about the login system and another about the payment system.
  • Every now and then, clean up old conversations. Delete ones you don’t need anymore to free up space.

Building the Right Context

Context is all the information the AI needs to understand your project. The more relevant context you provide, the better the AI’s work will be. One key part of context is setting clear rules and configurations.

Rules and Configuration Settings

Rules are like ground rules for your project. They help the AI generate code that fits your needs. Here’s how to set them up:

  • Be Clear: Define rules that tell the AI how to write code. For example: “If a file has more than 300 lines, split it into smaller files” or “Add a comment explaining each function.”
  • Keep Configurations Consistent: Configurations are settings that control how the project works (like which version of a tool to use). Make sure these settings are the same across your project – don’t use one version of Python in one file and a different version in another.
  • Write Down Special Rules: If there are exceptions to your rules (like “This one file can be longer than 300 lines because it’s a special case”), note them down. This prevents confusion.
  • Review and Update Rules: As your project grows, your rules might need to change. For example, if you start working with a new team, they might have different coding standards. Review your rules every few months to make sure they still make sense.
  • Build in Modules: A “module” is a small, self-contained part of code that does one thing (like a module for handling emails). Using modules makes your code easier to understand, test, and update – and it helps the AI generate code that fits into your project.

Examples of simple rules you might use:

  • If a function is longer than 20 lines, split it into smaller functions.
  • All variable names should be in plain English (no abbreviations like “usr” for “user”).
  • Every time you add a new feature, add at least one test for it.

Using the Right Tools

AI coding tools work best when you use them alongside other good software development tools. Think of it like cooking: a good chef needs more than just a stove – they need knives, pots, and fresh ingredients. For coding, these “ingredients” are best practices and tools that help keep your code clean and reliable.

Here are some tools and practices that work well with AI coding:

  • Static Code Analysis: These tools check your code for mistakes without running it – like spelling errors in variable names or code that might cause crashes. The AI can use these tools to fix its own code before you even see it.
  • Code Coverage: This tool tells you how much of your code is tested. If only 50% of your code is tested, the AI can help you write tests for the other 50%.
  • CI/CD Pipelines: CI/CD stands for “Continuous Integration/Continuous Deployment.” It’s a set of tools that automatically test and deploy your code. For example, every time you add new code, the pipeline runs your tests. If they pass, it deploys the code to your app. The AI can work with these pipelines to fix code that fails tests.
  • Code Formatters: These tools make your code look neat (like indenting lines properly). The AI can use them to make its code match your project’s style.

When you give the AI a task, it can use these tools to check its own work, fix mistakes, and improve the code – all without you having to do extra work. This saves time and makes sure the code is high quality.

Best Practices for Version Control

Version control is like a time machine for your code. It keeps track of every change you make, so you can go back to an older version if something breaks. It’s essential for any project, especially when working with AI. Here’s how to use it well.

Make Meaningful Commits

A “commit” is when you save a set of changes to your code. Instead of saving once a day with a message like “Changes,” save smaller changes with clear messages. For example: “Fixed bug where login failed for emails with uppercase letters” or “Added ‘forgot password’ link to login page.”

The AI can help you write these messages – just show it the changes you made, and ask for a commit message. But always read it first to make sure it’s accurate. Good commit messages make it easy to find past changes if you need to undo something.

Use a Branch Strategy

Branches are like copies of your code where you can work on new features without messing up the main project. Here’s how to use them:

  • Keep the “main” branch (or “master” branch) for code that’s ready to use.
  • Create a new branch for each new feature or bug fix. Name it something clear, like “add-user-profile” or “fix-checkout-error.”
  • Once the feature is done and tested, merge the branch back into the main branch.

This way, if a new feature causes problems, you can easily remove it without affecting the rest of the project.

Keep Your Repository Organized

A “repository” is where all your code and files live. Keeping it neat makes it easier to find things and helps the AI understand your project structure. Here’s how:

  • Set rules early: For example, “All image files go in the ‘images’ folder” or “No file should be larger than 5MB.”
  • Tell the AI about these rules in your prompts. For example: “Create a new page for user settings. Put the HTML file in the ‘pages’ folder and the CSS in the ‘styles’ folder.”
  • If you’re working on a big project with many parts (like a website with a frontend and backend), consider a “monorepo” setup – where all parts live in one repository but are organized into separate folders.

By following these version control practices, you’ll keep your project organized, make it easier to collaborate (with humans or AI), and have a safety net if something goes wrong.

Vibe coding with AI is changing how we build software, but it’s still a tool that works best with human guidance. By following these tips – from writing good prompts to testing thoroughly and keeping good documentation – you can make the most of AI to work faster and create better code. Remember, the AI is there to help, but you’re the one driving the project. With the right approach, you’ll be able to balance AI assistance with your own expertise to build great things.