How I Use AI Coding Assistants (and How You Can Use Them Better)

AI coding assistants have become a hot topic. Some developers say they are terrible and slow them down, while others say AI helps them work faster and reduce mental load. So why is the experience so different? What are some people doing wrong, and what are others doing right?

This post explains how AI coding assistants actually work, how to give them the right context, and how you can get much better results by improving your prompts and your workflow.

1. AI Assistants Work With “Empty Boxes”

AI models are not magical coders who understand your project automatically. They only know what you tell them. Think of each model as an empty box (its context window). If you don’t fill that box with the correct and relevant information, the assistant will guess and usually guess wrong.

Before asking an AI assistant to perform a task, it needs to understand:

• Your framework and coding standards
• Your project structure
• The purpose of the feature
• Any useful packages or tools
• The current state of your code

Advanced editors like Cursor help here. They inject a high-quality system prompt and index your entire project. Because of that, Cursor can automatically pull relevant files when needed. Your local or basic AI tools probably can’t, because they never receive that system prompt or indexed context.

But even with advanced tools, you must still give specific instructions.

2. “Fix X Feature” Is Not a Prompt

One of the biggest mistakes developers make is giving vague instructions.
For example:

“Improve the UserController.”

This is too broad. The assistant doesn’t know what “improve” means. Instead, be specific about the problem and the expected solution:

“UserController.php is too large and difficult to maintain. Please refactor it into smaller classes and services based on SOLID principles. Move business logic into services or repositories and keep the controller clean.”

This works because:

• You define the problem
• You describe the desired direction
• You limit the scope

Give the assistant the same clarity you would give a new teammate.

3. Give Only the Context That the Task Needs

More context is not always better. If the agent is working on the payment module, you do not need to include the whole report module, notification module, or unrelated controllers. Too much context leads to:

• Confused output
• Higher token costs
• Slower responses

Keep context focused. Let the assistant “build bridges” only when the information is truly related.

4. Good Prompts Are Detailed and Transparent

If you already know what the error is, or why something is broken, tell the assistant directly. Provide:

• The problem
• The error message
• Log output or stack trace
• The response you expect
• Any constraints or rules

You don’t lose control when you do this, you gain better results. Don’t be secretive with information you already know.

5. Choose the Right Model for the Job

You wouldn’t hire a physicist to fix your plumbing. Model selection works the same way.

Use small models when:

• Tasks are small
• Context needs are minimal
• You want fast and cheap responses

Use larger models when:

• Tasks are complex
• You need bigger context windows
• You need deep reasoning

You don’t always need the biggest or smartest model. You need the right one.

6. Create Reusable Context Files

To improve your workflow, create a folder like .ai-context/ and add small, organized Markdown files:

• Project structure
• Development rules
• Packages and how they are used
• Environment differences
• Module explanations
• High-level architecture
• System flow diagrams
• Example usages

Keep the files small, focused, and non-repetitive. Link between files when needed so the assistant can navigate them.

For example, if you use a library or package:

1. Create a small .md file that explains the library with simple examples.
2. Add it to .ai-context/packages/.
3. In your main architecture file, just reference it.

This creates a knowledge base the AI can rely on, just like onboarding documentation for new developers.

7. Use AI to Improve Your Prompts

If you don’t feel confident in English (B2 or below), ask another AI model to rewrite your prompt:

“Please improve this prompt for coding agents.”

This is a simple trick but makes a big difference in code quality.

Conclusion

AI coding assistants are powerful tools, but only when we use them correctly. They are not mind readers, and they cannot magically understand your entire project without the right context, structure, and guidance. When you give them focused information, clear problems, and well-prepared prompts, they become real partners that can speed up your work and reduce your mental load.

Remember: be specific, stay organized, give only the context that matters, and choose the right model for the task. Treat the AI like a new teammate who needs directions, not a machine that knows everything. If you build a good system around how you give tasks, you will get reliable, consistent, and high-quality results.

AI will not replace developers but developers who know how to work with AI will definitely have an advantage.