Skip to content
BSWEN
Blog

How to Maintain Code Comprehension When Using AI Tools Without Losing Critical Understanding

Cowrie
Cowrie
Dev @ Bswen

Problem

When I use AI tools like GitHub Copilot or ChatGPT to generate code, I can work faster. But I noticed something troubling: when I need to debug that code later or modify it for a new feature, I struggle to understand what it actually does.

I recently asked myself: “Am I losing my ability to understand code by letting AI write it for me?”

The problem is real. Other developers report the same issue - they get code that works, but their understanding of it feels “flimsy.” When bugs appear or requirements change, they’re stuck.

What happened?

Let me share a concrete example. I was working on a user data processing function and asked an AI tool to generate it.

AI-generated code I didn't understand
function processUserData(users) {
  const processed = users.map(user => ({
    ...user,
    status: user.age > 18 ? 'adult' : 'minor',
    lastActive: new Date().toISOString(),
    priority: user.premium ? 'high' : 'low'
  }))
  return processed.filter(p => p.status !== 'suspended')
}

The code worked perfectly in my tests. I moved on to other tasks.

But two weeks later, a bug report came in: “Premium users aren’t being filtered correctly.” I stared at this function for 20 minutes. I couldn’t remember:

  • Why I was filtering by status !== 'suspended' when the status field only held ‘adult’ or ‘minor’
  • Whether the filter should come before or after the map transformation
  • How the priority logic related to the suspension check

I had accepted the AI-generated code without truly understanding it. This is what I call “flimsy understanding.”

How to solve it?

I tried several approaches to maintain code comprehension while still using AI tools.

First attempt: Just read the code more carefully

I started forcing myself to read every line of AI-generated code before accepting it. But this wasn’t enough. Reading alone didn’t help me retain the logic. It felt like studying for a test by skimming the textbook - I recognized the code but couldn’t explain why it worked.

Second attempt: Add my own documentation

I began adding comments to explain the AI-generated code in my own words:

Code with my comprehension added
function processUserData(users) {
  // Transform each user: determine status, timestamp, and priority
  const processed = users.map(user => ({
    ...user,
    status: user.age > 18 ? 'adult' : 'minor',
    lastActive: new Date().toISOString(),
    priority: user.premium ? 'high' : 'low'
  }))


  // Remove suspended users (this was the bug - status never gets set to 'suspended'!)
  return processed.filter(p => p.status !== 'suspended')
}

When I wrote that comment about the bug, I realized the issue: the AI had generated code that checks for a status value that never gets set. I wouldn’t have caught this just by reading.

Third attempt: Modify the code intentionally

Now I follow a different workflow. When AI generates code, I don’t just accept it. I:

  1. Break it into named steps: Extract complex logic into well-named variables or functions
  2. Add logging: Insert console.log statements to see what’s happening
  3. Test edge cases: Try inputs that should fail
  4. Rewrite parts myself: Change at least one section to my own implementation

Here’s the same function rewritten with my understanding baked in:

Code I truly understand
function processUserData(users) {
  const processed = users.map(user => {
    // Determine if user is adult or minor based on age
    const status = user.age > 18 ? 'adult' : 'minor'


    // Track when we processed this user
    const lastActive = new Date().toISOString()


    // Premium users get high priority
    const priority = user.premium ? 'high' : 'low'


    // Return user with all computed fields
    return {
      ...user,
      status,
      lastActive,
      priority
    }
  })


  // Remove suspended users (filtered separately, not by status field)
  const activeUsers = processed.filter(p => !p.isSuspended)


  console.log(`Processed ${users.length} users, ${activeUsers.length} active`)


  return activeUsers
}

I can explain every line of this code. I know why it works. I can modify it confidently when requirements change.

Fourth attempt: Practice without AI

I also set aside time to code without AI assistance. I pick small problems - implementing a sorting algorithm, writing a recursive function, solving a coding challenge - and I don’t use any AI tools.

This is like going to the gym. I’m strengthening my core coding muscles so they don’t atrophy even while I use AI to handle routine tasks.

The reason

The key reason AI code creates flimsy understanding is that passively consuming code doesn’t build the same mental models as actively writing it.

When you write code yourself, you:

  • Make dozens of small decisions about variable names, logic flow, and edge cases
  • Experience the friction of getting stuck and figuring it out
  • Build a mental map of how the pieces fit together

When you just read AI-generated code, you skip that friction. The code looks perfect, but your brain didn’t do the work of creating it. So the understanding doesn’t stick.

Think of it like watching someone else solve a puzzle versus solving it yourself. The watcher sees the solution, but the solver understands the journey.

Why this matters

Maintaining code comprehension matters for four practical reasons:

1. Debugging becomes harder: When you don’t understand the code, you can’t efficiently find and fix bugs. You’ll spend hours staring at code that you should be able to troubleshoot in minutes.

2. System architecture gets murky: Without deep understanding of how components interact, you can’t make good architectural decisions. You’ll create fragile systems that break when requirements change.

3. Team collaboration suffers: Code reviews become superficial when everyone accepts code they don’t fully understand. Knowledge silos form, and bus factor increases.

4. Career growth stalls: Technical interviews assess your understanding, not your ability to prompt AI. You need core skills to advance to senior roles.

Summary

In this post, I showed how to maintain code comprehension while using AI tools. The key point is to be an active participant, not a passive consumer.

Break AI-generated code into named steps. Add your own documentation. Test edge cases. Rewrite parts yourself. And regularly practice coding without AI assistance to keep your core skills sharp.

AI tools can make you faster, but only you can make yourself a better developer.

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: