AK DevCraft

Posted on Mar 14

Practical Tips When Working with AI Coding Assistants

#ai #softwaredevelopment #softwareengineering #githubcopilot

Background

Modern AI coding assistants like Claude, GitHub Copilot, and ChatGPT can dramatically accelerate development. Recently, while working on a feature update, I had to modify an existing API to fetch data from a new system while maintaining backward compatibility.

The migration was gradual. Some clients would continue using the old system for a while, while others would start using the new one. Because of that, the implementation had to support both behaviors during the transition period.

Like many developers today, I used an AI coding assistant to speed up the implementation.

At first, it seemed straightforward.

But the process turned out to be more interesting than expected.

The First Iteration Looked Correct… But Wasn't Ideal

The AI-generated code worked functionally. It handled the new system integration, preserved backward compatibility, and integrated with the existing service.

But after reviewing the code carefully, a few issues surfaced:

Extra conditional branches that were unnecessary
Redundant logic left over from earlier iterations
Code paths that technically worked but were not optimal
Some defensive checks that were never needed for the actual use case

In other words, the code worked, but it wasn't clean.

It took multiple iterations and careful review before the implementation reached a version that was both correct and maintainable.

This experience reinforced something important.

AI Accelerates Coding — Not Thinking

AI assistants are excellent at generating working code quickly. They help remove boilerplate, explore possible implementations, and reduce the time spent writing repetitive logic.

However, they do not fully understand the context of your system.

They don't know:

The long-term architecture decisions
The migration strategy
The constraints of your system
What future developers will have to maintain

Because of this, AI often generates code that is technically correct but contextually imperfect.

And that is where code review becomes critical.

In the AI Era, Code Review Becomes a Core Skill

When development speed increases, the risk of suboptimal code entering the codebase also increases.

If developers accept AI-generated code in the first iteration, teams may gradually accumulate:

Unnecessary abstractions
Redundant logic
Hidden technical debt
Dead code paths

Over time, these small issues compound, making systems harder to maintain.

This means developers must become even better reviewers than before.

Good code review is no longer just about catching bugs. It is about evaluating whether the code truly fits the system.

Questions We Should Now Ask During Code Review

When reviewing AI-assisted code, now intentionally ask a few additional questions.

1. Does the code solve the problem exactly, or more than necessary?

AI often introduces extra flexibility that the use case does not require.
Extra flexibility today can easily become unnecessary complexity tomorrow.

2. Is there redundant or leftover logic?

Because AI suggestions often evolve across multiple prompts, some intermediate logic can remain even after the final version is generated.
This can result in code paths that are never actually used.

3. Is the implementation optimal for the system architecture?

AI may suggest patterns that work in general but do not align with your system's architecture.

Examples include:

Introducing unnecessary abstractions
Overusing defensive checks
Adding layers that increase complexity without real benefit

4. Will the code still make sense to the next developer?

Maintainability matters.
Even if the code works, the question remains:

Would another engineer understand this logic six months from now?

AI Changes the Development Workflow

One interesting shift I’ve noticed is that development is starting to look more like collaboration between a developer and an AI assistant.

The workflow increasingly looks like this:

Developer defines the intent
AI generates an implementation
Developer reviews and refines
AI proposes improvements
Developer validates architecture and constraints

The developer's role shifts slightly from writing every line of code to evaluating, refining, and validating generated code.

The Real Skill Shift for Engineers

As AI becomes more capable at writing code, the value of engineers will increasingly come from their ability to:

Review code critically
Evaluate trade-offs
Optimize implementations
Ensure architectural alignment

In other words, the skill of thinking about code becomes even more important than writing code.

Development Flow Diagram

Here is a quick comparison between Traditional and AI-Assistent development flow.

Traditional Development

Developer
↓
Write Code
↓
Code Review
↓
Merge

AI-Assisted Development

Developer Idea
↓
AI Generates Code
↓
Developer Reviews & Refines
↓
AI Iteration
↓
Code Review
↓
Merge

AI accelerates code generation, but developers are still responsible for validating the architecture, reducing unnecessary complexity, and ensuring maintainability.

Final Thoughts

AI tools are powerful accelerators for software development. They can help teams move faster and explore solutions more quickly.

But speed should not come at the cost of quality.

If anything, the rise of AI in development makes strong code review practices more important than ever.

Ultimately, AI can generate code, but engineers are still responsible for the systems they build.

If you have reached here, then I have made a satisfactory effort to keep you reading. Please be kind enough to leave any comments or share any corrections.

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