Skip to content
BSWEN
Blog

How Should Developers Use AI Coding Assistants Effectively?

Cowrie
Cowrie
Dev @ Bswen

The Real Problem with AI Coding Assistants

I’ve seen developers frustrated by watching colleagues use AI tools to generate code they clearly don’t understand. The sentiment from a popular Reddit thread captures it: “AI really killed programming for me.” But the problem isn’t the AI - it’s how developers use it.

The core issue: developers with weak fundamentals are using AI to generate code they cannot explain. This creates technical debt, security vulnerabilities, and unmaintainable codebases. Code reviews become meaningless when the author cannot explain their own code.

Let me share what I’ve learned about using AI assistants effectively.

The Fundamental Rule: Understanding Over Output

The most important principle is simple: never commit code you cannot explain.

If asked to explain a function in a code review, you should be able to describe:

  • What it does and why
  • Edge cases it handles (and misses)
  • Why this approach over alternatives

I found this principle in practice. When I started using AI assistants, I would sometimes accept suggestions without fully understanding them. Then during code reviews, I’d struggle to explain the logic. That was a sign I was using AI wrong.

Now I apply a simple test: “Could I whiteboard this solution?” If not, I don’t commit it until I understand it.

Five Rules for Effective AI Usage

Rule 1: Use AI for Acceleration, Not Replacement

AI should speed up what you already know how to do. Let it handle boilerplate, suggest patterns, or explore solutions. But you drive the architecture and logic.

Division of responsibility
What AI Should Do          | What You Should Do
---------------------------|---------------------------
Write boilerplate code     | Define architecture
Suggest patterns           | Choose which pattern to use
Generate test cases        | Verify test coverage
Explore alternatives       | Make the final decision
Speed up iteration         | Ensure correctness

Rule 2: Verify Everything

AI confidently produces incorrect code. I’ve seen it suggest APIs that don’t exist, use deprecated methods, and introduce subtle bugs. The habit of verification is non-negotiable.

When AI suggests code, I check:

  • Does this API actually exist? (Check documentation)
  • Are there edge cases not handled? (Think through scenarios)
  • Does this match our project’s patterns? (Review similar code)
  • What happens when this fails? (Consider error paths)

Rule 3: Iterate Deeper, Not Wider

The real productivity gain from AI isn’t writing code faster - it’s exploring more solutions in the same time.

Instead of accepting the first output, I use AI to:

  • Generate three different approaches
  • Compare trade-offs between solutions
  • Refine the chosen approach through multiple iterations
  • Test edge cases I wouldn’t have considered

This is how good developers become “more productive with higher quality work” as one experienced developer noted. AI enables deeper exploration, not just faster typing.

Rule 4: Build Fundamentals First

AI is most valuable when you have enough knowledge to ask the right questions and evaluate answers. Without fundamentals, you can’t distinguish good suggestions from bad ones.

I’ve seen developers struggle when they skip learning core concepts:

  • They can’t evaluate if AI’s solution is optimal
  • They miss obvious security issues
  • They accept overly complex solutions
  • They can’t debug when things go wrong

Rule 5: Ask for Exploration, Not Just Solutions

The quality of AI output depends heavily on how you ask.

Prompt quality comparison
Poor prompt: "Write me a caching function"
Better prompt: "I need to cache API responses. What are different caching
              strategies and their trade-offs?"
Best prompt: "Show me in-memory cache, Redis, and localStorage options for
             API response caching. Compare them for a single-page app with
             moderate traffic."

The better prompt lets AI teach you about the problem space, then you make an informed choice.

Common Mistakes I’ve Seen (and Made)

Copy-pasting without reading. Accepting AI suggestions without understanding creates black boxes in your codebase. When something breaks, you’re stuck.

Skipping fundamentals. Relying on AI before learning core concepts limits your ability to evaluate its output. You become dependent rather than augmented.

Treating AI as oracle. AI produces plausible-sounding but incorrect code. Always verify against documentation and tests.

Over-abstracting. AI often suggests complex solutions when simple ones suffice. Understand the trade-offs before accepting complexity.

Ignoring context. AI lacks your project’s history, constraints, and conventions. Its suggestions need adaptation to fit your codebase.

The Verification Pattern

Here’s how I verify AI suggestions in practice:

  1. Read the code line by line - If I can’t explain each line, I don’t use it
  2. Check against documentation - APIs change, methods get deprecated
  3. Consider error paths - What happens when the network fails? When data is malformed?
  4. Run tests - Never assume AI output is correct
  5. Review against project patterns - Does this match how we write code?

Why This Matters Long-Term

Software development is fundamentally about problem-solving, not code production. Understanding is required for debugging, scaling, and maintaining systems.

AI tools are evolving rapidly. The developers who thrive will be those who adapt while maintaining core competencies. Technical debt from AI-generated but misunderstood code compounds quickly.

Career growth depends on demonstrable expertise, not just shipped features. When you can explain your code in interviews, reviews, and debugging sessions, that’s real value.

Summary

In this post, I shared five rules for using AI coding assistants effectively: never commit code you can’t explain, use AI for acceleration not replacement, verify everything, iterate deeper not wider, and build fundamentals first.

The key insight from experienced developers: AI makes good developers more productive and bad developers more destructive. The difference isn’t the tool - it’s how you use it.

Before using AI-generated code, ask yourself: “Could I explain this in a code review?” If the answer is no, take the time to understand it first.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

Comments

Tags: