AI Coding Assistants Are Useless Without THIS

Imagine having a super-smart coding assistant, like GitHub Copilot, Cursor, or Windsurf. They’re incredibly powerful, but they’re not mind-readers! Just like you need clear instructions to follow, AI coding assistants need context to understand what you want. Here i will introduce you to “rules files,” which help you provide that context, making your AI assistant a much more reliable partner.

Instead of just throwing vague prompts at your AI and hoping for the best, we can use rules files to create a “project handbook” for your AI. These files tell the AI about your project, how it’s structured, and what technologies you’re using. Think of it as giving your AI assistant a detailed briefing before they start working!

What are Rules Files?

Rules files are special text documents that sit at the root of your project. They act like configuration files, but specifically designed to give context to your AI coding assistant. Major code editors now support rules files, each using slightly different file names:

• VS Code (with GitHub Copilot): .github/copilot-instructions.md
• Cursor: .cursorrules
• Windsurf: .windsurfrules

These files contain instructions that are considered during every AI interaction – whether you’re chatting, generating code, or making edits. This ensures that the AI is always aware of the project’s specific requirements and style.

The Golden Rule: Verification

Before we dive into the contents of a rules file, let’s add a crucial line to verify our rules are being read correctly.

The first line of your rules file should always be:

Every time you choose to apply a rule, explicitly state the rule in the output. (Rule: "Explicit Rules")

• What does this do? It tells the AI to tell you which rules it is using in its output. This allows you to check if your rules are being applied as expected.
• Why is it useful? It’s easy to make a typo or mistake in the rule file. This simple check helps you spot such mistakes early.

Here’s an example output, showing that the rules are being applied correctly:

Explicit Rules: Project Context, Tech Stack, UI Style, Consistent Imports

Core Components of a Rules File

Let’s break down the key elements of a rules file:

Project Context

• What is it? A brief overview of the project’s purpose or goal.
• Why it matters? Provides the big picture, guiding the AI’s understanding of the code’s intent.
• Example

Project Context: A system to help users track their daily expenses and set budgets.

Project Structure

• What is it? Describes how the project is organized, including folders and their roles.
• Why it matters? Helps the AI place new files and functions in the right locations.
• Example:

Project Structure: Monorepo with a client app in /client (React), a server in /server (Node.js), and shared components in /shared.

Imagine you want a function to calculate tax. Without the structure rule, the AI might put it anywhere. With the rule, the AI knows to create a new file in /shared/utils/taxCalculator.js

Tech Stack

• What is it? A list of the technologies used in the project, such as programming languages, frameworks, libraries, etc.
• Why it matters? Prevents the AI from using technologies that you’re not using and ensures it generates code that is compatible with your tech stack.
• Example:

Tech Stack: React, Node.js, Express.js, PostgreSQL, MUI

If you have a special UI library, for instance, MUI or Ant Design you need to declare it. This way when you ask AI to create a new UI component it will use the library you want instead of creating basic HTML.

Specific Tech Details

• What is it? Contains specific implementation details about a particular tech.
• Why it matters? This allows fine-tuning the AI to generate code that aligns with your specific configuration or settings.
• Example: For a Chrome Extension

Chrome Extension: Use manifest V3, handle messaging securely, and minimize permissions. 

If you have particular permission rules, you can specify them. So the AI will make sure that any new code generated will respect these rules.

Preferences (Formatting, Naming etc.)

• What is it? Specific instructions regarding code style, formatting, and naming conventions.
• Why it matters? Ensures a consistent code style. Only add the rules when the AI is making mistakes.
• Example:

Code Preferences: Always use snake_case for database column names.

If you want consistent naming of variables or function names. You can define that here.

Other Useful Rules

• Readme Updates: Instruct the AI when to update the README file based on library changes.
• Commenting: Define comment rules for complex logic, critical system parts, and other specific cases.

Principles for Maintaining Rules Files

• Keep it Concise: Don’t overload your rules file with unnecessary information. Provide only the essential details. Remember that more isn’t better here; less and clear is what you’re looking for.
• Continuously Update: Think of your rules file as a living document. Update it as you notice new issues or as your project evolves. Treat this like code and include in your pull request.

Benefits of Rules Files

Using rules files can:

• Improve Consistency: Makes sure code adheres to established conventions.
• Improve Reliability: Reduce errors by making fewer assumptions.
• Save Time: Automates things like formatting, style, and dependency management.
• Generate More Accurate Code: Tailors the AI to your exact requirements.
• Make Code More Maintainable: Ensures all team members can easily understand code structure.

When Rules Files Matter Most

• When working on existing projects with their own conventions.
• As your projects become larger and more complex.
• When consistency and maintainability become important.