What I Learned Using AI to Troubleshoot a Mobile Game Development Problem

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What I Learned Using AI to Troubleshoot a Mobile Game Development Problem

What I Learned Using AI to Troubleshoot a Mobile Game Development Problem

By Boby Michael

Last Updated: August 2026

Introduction

Using artificial intelligence during software and game development sounds simple.

You describe a problem, ask an AI assistant for a solution, copy the suggested code, and expect everything to work.

My experience has been very different.

While working on a mobile game project, I encountered a problem that looked relatively straightforward at first: a gameplay interaction worked correctly inside the Unity Editor, but after building the project for Android, the same interaction stopped responding as expected.

The problem became more complicated because other parts of the game were still working.

The player could move. The Android build could launch. The game itself was running. But one important interaction that worked in the development environment was not behaving correctly on the mobile device.

I used AI as one part of my debugging process.

It helped me understand possible causes, organize my investigation, explain unfamiliar code, and suggest tests. At the same time, some of its early suggestions were too general or did not match the exact configuration of my project.

That experience taught me something important:

AI is extremely useful for debugging when you use it as an assistant, but it should not be treated as an automatic solution generator.

In this article, I explain what happened, how I investigated the problem, where AI helped, where its suggestions were wrong, and what I learned that may help other developers.

1. The Original Problem

The project was a Unity-based game that I was adapting for Android.

The original desktop version used keyboard and mouse controls for several gameplay interactions.

One of those interactions involved picking up and holding an object.

Inside the Unity Editor, the interaction worked.

I could test the game, approach an object, use the expected input, and the object could be picked up.

The problem appeared when I tested the Android build.

The mobile version used on-screen controls instead of relying only on a physical keyboard and mouse.

The player movement worked, but the pickup interaction did not respond correctly.

This created an interesting debugging situation.

The underlying gameplay feature already existed.

The object could be picked up in the desktop environment.

The problem was related to how the input and interaction were being handled in the mobile environment.

That distinction became important later.

The important question became:

Was the mobile button actually reaching the same gameplay logic used by the desktop input?

2. What I Expected to Happen

My expectation was relatively simple.

The mobile interface had a pickup button.

When the player pressed that button, the game should perform essentially the same gameplay action that occurred when I pressed the corresponding keyboard key during desktop testing.

The expected sequence was:

Player approaches object → presses mobile pickup button → game detects input → interaction code runs → object is picked up

Because the original gameplay system already worked in the Editor, I initially expected the mobile conversion to require only an input connection.

In other words, I thought the main task would be:

Mobile Button → Existing Pickup Function

That assumption turned out to be too simple.

3. What Actually Happened

The Android build behaved differently.

The game itself started normally.

Player movement worked.

The mobile interface appeared.

However, pressing the pickup button did not consistently trigger the same interaction that worked in the Editor.

While testing the mobile version, I also noticed another unexpected behavior: movement could affect the state of objects that I expected to remain held.

This was a clue that the problem might involve more than just the button's visual appearance.

The important question became:

Was the mobile button actually reaching the same gameplay logic used by the desktop input?

4. My Debugging Process

I divided the problem into smaller questions.

This was one of the most useful lessons from the experience.

Rather than asking:

“Why doesn't pickup work?”

I started asking:

  1. Is the mobile button receiving the press?
  2. Is the button sending an input event?
  3. Is the input manager recording that event?
  4. Is the gameplay script checking for that mobile input?
  5. Is the raycast detecting the object?
  6. Is the object allowed to be picked up?
  7. Does the object become attached to the player?
  8. Does another part of the game immediately release it?

This changed the debugging process considerably.

Instead of treating the entire feature as one problem, I could investigate each stage separately.

5. Checking the Mobile Input

The project contained a mobile input system designed to translate button presses into game input.

One of the functions used by the mobile input system recorded pressed buttons.

Conceptually, the system worked like this:

Mobile button press

↓

Mobile input manager

↓

Button name stored

↓

Gameplay script checks the button

↓

Gameplay action occurs

This meant that the first thing I needed to establish was whether the button press was actually reaching the input manager.

I added logging during testing so I could observe what happened when I pressed the Android button.

The important lesson here was simple:

Never assume an input button works just because the button appears on the screen.

A visible button and a functioning input event are two different things.

6. How AI Helped Me

This was where AI became useful.

Instead of asking an AI assistant a vague question such as:

“My Android pickup button doesn't work.”

I provided more context.

I explained:

  • The Unity environment
  • The platform
  • What worked in the Editor
  • What failed on Android
  • How the mobile input system was structured
  • The relevant scripts
  • The expected behavior
  • The actual behavior
  • The error messages and logs I was seeing

This produced much more useful suggestions.

AI helped me think through several possible causes.

For example, it suggested checking whether:

  • The mobile button was correctly connected
  • The button name matched the name expected by the gameplay script
  • The input event was actually being registered
  • The gameplay code was checking the mobile input
  • The raycast was being executed
  • The Android build was using the same input path as the Editor
  • Another script was changing the object's state

Not every suggestion was correct for my project.

But the suggestions helped me create a debugging checklist.

That was one of the most valuable uses of AI during the process.

7. Where AI's First Suggestions Were Wrong

This is probably the most important part of my experience.

AI can give an answer that sounds extremely confident even when it does not have enough information about your exact project.

Some early suggestions focused on changing the input implementation itself.

That sounded reasonable.

But after examining the project, I realized that changing the entire input system would have been unnecessary.

The project already had a mobile input mechanism.

The bigger question was whether the existing system was connected correctly to the gameplay code.

This taught me not to immediately implement the first solution an AI suggests.

Instead, I now ask:

What evidence supports this diagnosis?

That small change in thinking can save a lot of development time.

8. Why Context Matters When Using AI for Debugging

One of the biggest lessons I learned is that AI is highly dependent on the information you provide.

Compare these two questions.

Question 1

“Unity Android pickup button doesn't work. How do I fix it?”

This is extremely broad.

There could be hundreds of possible causes.

Question 2

“My Unity game has a pickup system that works with the keyboard in the Editor. On Android, player movement works but the pickup button doesn't trigger the interaction. I have a MobileInputManager that records button presses, and the gameplay script checks for the mobile pickup input alongside the keyboard input. What should I test to determine whether the failure is occurring at the UI button, input manager, gameplay condition, or object detection stage?”

The second question contains useful diagnostic information.

It tells the AI:

  • What platform is involved
  • What already works
  • What does not work
  • How input is structured
  • What the developer wants to investigate

The quality of AI assistance improved when I supplied better context.

9. Testing the Input Path

After breaking the problem into smaller parts, I focused on the actual input flow.

I wanted to know whether pressing the Android button generated the expected event.

A useful debugging sequence was:

Button

→ Did the button receive the touch?

Input Manager

→ Did the manager record the button?

Gameplay Script

→ Did the gameplay code detect the recorded input?

Object Detection

→ Did the raycast find the correct object?

Interaction Rules

→ Was the object actually eligible to be picked up?

Object State

→ Was the object successfully attached?

This approach prevented me from making random changes.

If the button press was never recorded, there was no reason to investigate the raycast.

If the input was recorded but the raycast failed, the problem was further down the chain.

This is a general debugging principle that I now use more often:

Find the first point where expected behavior becomes actual behavior.

10. The Importance of Testing on the Real Device

Another lesson was the difference between Editor testing and Android testing.

The Unity Editor is extremely useful.

It allows fast iteration and convenient debugging.

But an Editor test does not guarantee that the Android build will behave identically.

Mobile builds introduce additional considerations, including:

  • Touch input
  • Mobile UI
  • Different input paths
  • Screen resolution
  • Device performance
  • Platform-specific behavior
  • Build configuration

That is why I stopped treating the Editor as the final test environment.

For mobile projects, the real device became an important part of the development loop.

My workflow became closer to:

Change → Build → Install → Test → Observe → Diagnose → Change again

It takes more time than testing only inside the Editor, but it provides information that the Editor cannot always reproduce.

11. What I Changed

Rather than replacing the entire gameplay system, I focused on making the mobile input path communicate correctly with the existing interaction logic.

The key idea was to keep the gameplay behavior consistent.

The desktop input and mobile input could be different ways of triggering the same gameplay action.

Conceptually:

Keyboard Input

↓

Pickup Action

and:

Mobile Button

↓

Mobile Input

↓

Pickup Action

This is cleaner than creating two completely separate pickup systems.

It also makes future maintenance easier.

If the pickup behavior changes, I only need to maintain the gameplay logic rather than duplicating the entire system for every platform.

12. Why I Did Not Simply Rewrite Everything

When a feature doesn't work, it can be tempting to start again.

AI can make this temptation stronger because it can quickly generate alternative implementations.

But rewriting working code can introduce additional problems.

In my case, the original desktop interaction already provided evidence that the underlying pickup mechanic worked.

Therefore, I wanted to preserve the working part and investigate the platform-specific part.

This led to a useful rule:

Don't replace working systems until you understand why the existing system is failing.

Sometimes the smallest change is the correct solution.

13. The Final Result

After investigating the input path and making the necessary changes, I was able to move closer to the behavior I originally wanted for the mobile version.

The most important improvement was not simply getting a button to work.

It was understanding why the mobile interaction was behaving differently from the Editor version.

That understanding matters because future problems become easier to diagnose.

If another mobile button stops working, I now know to investigate the complete path:

UI → Input → Gameplay → Detection → State

rather than immediately replacing code.

14. What AI Actually Contributed

Looking back at the experience, AI did not solve the problem by itself.

That is an important distinction.

My role was still to:

  • Reproduce the problem
  • Inspect the project
  • Provide context
  • Read the code
  • Test suggestions
  • Reject incorrect suggestions
  • Build the project
  • Test on Android
  • Observe the result
  • Decide which solution made sense

AI helped with:

Explaining

It helped me understand unfamiliar parts of code and terminology.

Brainstorming

It provided possible causes I could investigate.

Debugging Structure

It helped turn a vague problem into a checklist.

Code Suggestions

It could suggest implementation approaches that I could test.

Documentation

It helped me organize what I had learned.

But the final decision still came from testing the actual project.

15. What AI Could Not Know Automatically

An AI assistant does not automatically have complete knowledge of your development environment.

It may not know:

  • Your exact Unity project configuration
  • Your complete script structure
  • Which packages are installed
  • Which input system is active
  • How your UI is connected
  • What happens on your particular Android device
  • What changes you made elsewhere in the project

This is why AI sometimes produces solutions that look technically correct but don't solve the actual problem.

A code snippet can be valid in isolation and still be wrong for a particular project.

That distinction is important for developers using AI.

16. Mistakes I Made During the Process

Debugging also showed me some mistakes in my own approach.

Mistake 1: Assuming the Button Was the Problem

At first, it was easy to focus on the visible mobile button.

But the button was only one part of the complete system.

The real investigation needed to follow the input all the way into the gameplay logic.

Mistake 2: Trying Solutions Too Quickly

An AI assistant can generate solutions very quickly.

That can create a dangerous habit:

Problem → AI answer → Copy → Test

A better process is:

Problem → Understand → Hypothesis → Test → Implement

AI can help with the middle stages, but the developer still needs to control the process.

Mistake 3: Not Providing Enough Context

My early questions were not detailed enough.

When I provided more information about:

  • The project
  • The scripts
  • The platform
  • The symptoms
  • The expected behavior

the suggestions became more useful.

17. A Better AI Debugging Workflow

Based on this experience, this is the workflow I recommend to other developers.

Step 1: Describe the Environment

Tell the AI:

  • Engine
  • Version
  • Platform
  • Programming language
  • Relevant packages

For example:

“I am developing a Unity game for Android.”

Step 2: Describe the Expected Behavior

Explain exactly what should happen.

Example:

“When the player presses the pickup button near an object, the object should become held by the player.”

Step 3: Describe the Actual Behavior

Be precise.

Example:

“The feature works in the Unity Editor but does not respond in the Android build.”

Step 4: Provide Relevant Code

Don't send your entire project if it isn't necessary.

Start with the scripts directly involved in the problem.

Step 5: Include Errors and Logs

If you have an error message, provide the complete message.

Don't summarize it as:

“There is some error.”

The exact error can dramatically change the diagnosis.

Step 6: Ask for Possible Causes

Instead of asking:

“Give me the code.”

ask:

“List the most likely causes and explain how I can test each one.”

This encourages diagnosis instead of blind implementation.

Step 7: Test One Change at a Time

If you change five things simultaneously and the problem disappears, you may not know which change fixed it.

Small controlled tests are easier to understand.

18. Lessons for Other Game Developers

My experience produced several lessons that I think are useful beyond this particular project.

Lesson 1: AI is a debugging assistant, not a debugging button.

You still need to reproduce and understand the problem.

Lesson 2: Give AI context.

The better the information, the more useful the suggestions tend to be.

Lesson 3: Test AI-generated code.

Never assume that code is correct simply because it looks professional.

Lesson 4: Separate symptoms from causes.

A button that doesn't work may actually be an input, event, raycast, state, or configuration problem.

Lesson 5: Test on the target platform.

An Editor result does not guarantee identical mobile behavior.

Lesson 6: Don't rewrite working systems unnecessarily.

Find the broken connection first.

Lesson 7: Keep human judgment in the development loop.

The developer understands the goals and constraints of the actual project.

19. When Should Developers Use AI?

I have found AI particularly useful for tasks such as:

  • Explaining error messages
  • Understanding unfamiliar code
  • Brainstorming solutions
  • Creating debugging checklists
  • Exploring alternative implementations
  • Writing documentation
  • Generating test ideas
  • Learning new programming concepts

However, I would be more careful when using AI for:

  • Security-sensitive code
  • Financial systems
  • Authentication
  • Privacy-related functionality
  • Production-critical systems
  • Complex architectural changes

In these situations, AI can still be useful, but human review and proper testing become even more important.

20. My Biggest Lesson

The biggest lesson I learned was not about Unity.

It was about how I use AI.

Before working through this kind of problem, it was tempting to think of AI as a system that should provide the answer.

Now I think of it differently.

I use AI to help me investigate.

Instead of asking:

“What is the answer?”

I often ask:

“What could be causing this, and how can I test each possibility?”

That small change makes AI much more useful.

It turns the conversation from simply requesting code into a form of technical problem-solving.

21. AI Did Not Replace the Developer

This experience also changed how I think about the relationship between AI and software development.

AI did not:

  • Build the entire game for me
  • Understand every project dependency
  • Know exactly what was happening on my device
  • Automatically identify the correct cause
  • Test the final build for me
  • Decide which solution was best

I still had to do those things.

What AI provided was another way to think about the problem.

That can be extremely valuable.

For an independent developer working across multiple projects, having an assistant available to explain code, suggest possibilities, and help organize a debugging process can save time.

But the final responsibility remains with the developer.

22. A Simple Formula I Now Use

When I encounter a technical problem, my process is increasingly:

Reproduce → Describe → Investigate → Ask AI → Test → Verify → Implement → Test Again

Not:

Ask AI → Copy → Hope

The second approach may be faster for a very simple problem.

The first approach is much safer for a real project.

23. How This Experience Changed My Development Workflow

I now try to keep better records of problems I encounter during development.

When something goes wrong, I try to document:

  • What I expected
  • What happened
  • What I changed
  • What I tested
  • What worked
  • What failed
  • What I learned

This has another advantage.

A problem that happens again becomes easier to solve.

Instead of starting from zero, I have a record of previous experiments.

AI can also work more effectively when I provide that history.

24. Final Thoughts

Using AI to troubleshoot my mobile game development problem taught me that the most valuable part of AI-assisted development is not always the code it generates.

Sometimes the greatest value comes from helping a developer think.

AI can suggest possibilities.

It can explain unfamiliar concepts.

It can identify things worth checking.

It can help organize a complicated problem.

But the developer still needs to reproduce the problem, examine the project, test hypotheses, reject incorrect suggestions, and verify the final result.

My experience with the mobile pickup problem is a good example.

The feature worked in the Editor.

It behaved differently on Android.

The first AI suggestions were not all correct.

More context improved the suggestions.

Breaking the problem into smaller stages made the debugging process easier.

And testing on the actual target platform was essential.

The most important lesson I took away is simple:

Use AI to expand your problem-solving ability, not to replace your problem-solving ability.

That is the approach I now prefer when using AI in my own development projects.

Related AI Guide

If you are new to artificial intelligence and want to understand the technology behind AI tools, machine learning, deep learning, and generative AI, I have also written a comprehensive beginner's guide:

What Is Artificial Intelligence (AI)? A Complete Beginner's Guide to How AI Works

That guide explains the fundamentals of AI, how modern AI systems work, major AI technologies, practical applications, limitations, and my experience using AI across creative and technical projects.

You can explore the complete guide on BobyMichael.com.

About the Author

Boby Michael

Boby Michael is an independent creator and digital project developer with practical experience exploring game development, mobile projects, website publishing, creative content, and AI-assisted workflows.

His work focuses on experimenting with technology through practical projects rather than simply discussing concepts in theory.

Through these projects, Boby explores how artificial intelligence can support creativity, technical problem-solving, software development, and digital production while keeping human testing, judgment, and decision-making at the center of the process.

Article Details

Author: Boby Michael

Category: Artificial Intelligence / Game Development / Technology

Content Type: Personal Experience & Technical Case Study

Last Updated: August 2026