Working With AI in Coding the Right Way

🔹 Introduction

Artificial intelligence is transforming software development. Today, tools like Codex, GitHub Copilot, and LLM-powered editors such as Neovim and VSCode integrations can autocomplete entire functions, generate boilerplate, or even explain cryptic error messages. For many developers, it feels like having an extra set of hands on the keyboard.

But here’s the truth: AI isn’t a replacement for developers — it’s a powerful collaborator. Using it without a clear method can cause dependency, security gaps, or unreadable code. The real benefit comes when AI becomes your pair programmer — helping you code faster, learn continuously, and maintain control over the quality of your work.

This article explains how to work with AI in coding the right way, with strategies, do’s and don’ts, and workflow examples you can apply immediately.

🔹 1. Use AI as a Pair Programmer, Not an Autopilot

Imagine a junior developer sitting next to you. You wouldn’t just hand them the entire project and say, “Good luck.” Instead, you’d guide them, review their work, and use their input as a springboard. That’s how you should treat AI.

✅ Right way: Ask AI to scaffold a utility function, propose test cases, or show alternative implementations. Then refine, debug, and optimize manually.
❌ Wrong way: Let AI generate hundreds of lines of code and push directly to production.

Example: Instead of asking “Write me a full Django e-commerce app”, try:

“Generate a Django model for an Order with fields for status, payment method, timestamps, and a relationship to a User.”

This keeps the scope narrow, code review manageable, and architecture decisions in your hands.

🔹 2. Work in Small, Iterative Steps

Large prompts may feel efficient, but they often create bloated or inaccurate results. A better approach is to break down your tasks into smaller, testable pieces.

✅ Generate a function → Add validation → Write tests → Document it.
❌ Ask AI to produce an entire service layer in one go.

This method mirrors agile development: short cycles, frequent checks, and incremental improvements. It ensures you remain the decision-maker, while AI accelerates the busy work.

Pro tip: When debugging, feed AI only the relevant snippet and the error message instead of your whole project. This narrows its focus and prevents noise.

🔹 3. Control Coding Style and Standards

AI generates code in whatever style it has learned — which may not match your project. Without guidance, you risk ending up with inconsistent naming, formatting, or even outdated practices.

Add prompt instructions:
“Follow PEP8,” “Use async/await,” “Include type hints and docstrings.”
Run linters and formatters (e.g., black, eslint) after every AI-assisted session.
Use AI to generate tests and comments that align with your documentation style.

This ensures that your team (or future you) can understand and maintain the codebase.

🔹 4. Use AI for Learning, Not Just Output

The real value of AI isn’t just faster typing — it’s accelerated learning. By asking “why” and not just “what,” you can turn every AI session into a micro-lesson.

Ask for explanations:
- “Explain what this regular expression matches step by step.”
- “What’s the difference between using asyncio.gather and await sequentially in this code?”
Request comparisons:
- “Show me two different implementations of this caching logic, one with Redis and one in-memory.”
Explore alternatives:
- “Refactor this query using SQLAlchemy’s ORM style instead of raw SQL.”

When used this way, AI becomes a teacher that speeds up your path to mastery instead of a crutch that weakens your fundamentals.

🔹 5. Know the Limits and Risks

AI is powerful, but it is not infallible. It can hallucinate APIs, recommend insecure practices, or suggest inefficient patterns. Blind trust can create hidden technical debt.

Review suggestions as carefully as you would a junior developer’s code.
Watch for security flaws, such as unsanitized inputs, hardcoded secrets, or missing error handling.
Be aware of legal and licensing issues — code generated from public training data might not be safe to redistribute without checking.
Protect sensitive data. Never paste secrets, tokens, or proprietary algorithms directly into prompts.

The right approach is to use AI as a tool, not as a replacement for due diligence.

🔹 6. Integrate AI Smoothly Into Your Workflow

One of the best parts of modern AI coding is how easily it integrates into editors like Neovim, VSCode, or JetBrains IDEs. But how you configure it makes a big difference.

Combine structured and inline completions:
- Use nvim-cmp or IntelliSense for LSP-based completions (standard library, imports, snippets).
- Use virtual_text ghost suggestions for AI-assisted “flow” typing (like Copilot or Codex inline mode).
Assign keymaps for efficiency:
- <C-Space> → trigger popup completion.
- <C-y> → accept AI ghost suggestion.
- <C-n>/<C-p> → navigate multiple candidates.
Divide responsibilities:
- Let AI handle repetitive boilerplate (serializers, migrations, configs).
- Keep core architecture, security, and business logic human-driven.

This hybrid setup gives you speed without losing control.

🔹 Conclusion

AI coding tools are revolutionizing development. They can autocomplete, debug, refactor, and even teach — but only if you use them wisely.

The “wrong way” is to treat AI as a full autopilot. That leads to dependency, messy code, and missed learning opportunities. The “right way” is to treat AI as your pair programmer: