I Used AI to Write Code from Prompts-Here’s What Went Wrong

Efficiency and innovation: the twin gods of modern game development. As developers, we're constantly searching for groundbreaking tools, and AI, ...

especially in code generation, is rewriting the rules. This isn't just a personal anecdote, but a firsthand account of AI-assisted programming. It reveals the exciting breakthroughs and brutal lessons learned when you entrust the essence of your game to a machine.

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1. Understanding the Promise of AI-Powered Code Generation
2. The Experiment: Using AI to Write Code from Prompts
3. Reflections on the Experience
4. Conclusion: A Step Forward with Caution

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1.) Understanding the Promise of AI-Powered Code Generation

First, let's clarify what AI-powered code generation is and how it can be beneficial for game developers. AI models like GPT (Generative Pre-trained Transformer), which are trained on vast amounts of data, can generate code snippets based on natural language prompts. This approach promises to reduce the time spent writing boilerplate code, allow for quicker prototyping, and even inspire innovative solutions.

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2.) The Experiment: Using AI to Write Code from Prompts

I decided to experiment with using an AI tool to write code for a small game development project. My goal was to see if I could leverage this technology to generate parts of the game's backend logic, specifically focusing on data management and some basic gameplay mechanics.

Step 1: Formulating Clear Prompts

To get started, I crafted clear and specific prompts to guide the AI in generating the code. These included details about the project requirements, desired functionalities, and programming language preferences (in my case, Python).

# Prompt Example


**Prompt:** Generate a Python script for managing game data using a SQLite database. The script should include functions for adding new players, tracking their scores, and retrieving high scores.

Step 2: Generating Initial Code Snippets

The AI tool churned out code snippets based on my prompts. These ranged from import statements to full-fledged function definitions tailored to manage player data in a game database.

# Generated Code Example
import sqlite3

def initialize_database():
conn = sqlite3.connect('game_data.db')
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS players (id INTEGER PRIMARY KEY, name TEXT, score INTEGER)''')
conn.commit()
conn.close()

def add_player(name, score):
conn = sqlite3.connect('game_data.db')
c = conn.cursor()
c.execute("INSERT INTO players (name, score) VALUES (?, ?)" (name, score))
conn.commit()
conn.close()

def get_high_scores():
conn = sqlite3.connect('game_data.db')
c = conn.cursor()
c.execute("SELECT name, score FROM players ORDER BY score DESC LIMIT 10"
high_scores = c.fetchall()
conn.close()
return high_scores

Step 3: Evaluating the Generated Code and Incorporating It into the Project

The generated code was integrated into my project, and I proceeded to test its functionality in a controlled environment. This involved running tests to ensure that adding players, updating scores, and retrieving high scores worked as expected.

Step 4: Identifying Issues and Challenges

During testing, several issues became apparent:


- Lack of Error Handling: The generated code did not include error handling mechanisms, which led to runtime errors when the database operations failed (e.g., due to network issues or database corruption).

# Example of missing error handling
except sqlite3.Error as e:
print(f"An error occurred: {e}"

- Incompatibility with Project Environment: The AI tool's output was specific to the dataset and language model it had been trained on, which sometimes led to syntax errors or runtime issues when used in a different Python environment.


- Limited Adaptability: The generated code did not easily allow for custom modifications without disrupting its intended functionality, making future maintenance difficult.

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3.) Reflections on the Experience

This experiment highlighted several key points:

Importance of Fine-Tuning Prompts

The quality and specificity of the prompts significantly influenced the output of the AI. Poorly formulated prompts led to code that was either overly complex or insufficient for project needs. A better approach is to refine prompts based on initial outputs, iteratively refining until a satisfactory result is achieved.

Balancing Automation with Manual Coding

While AI can accelerate development and potentially generate novel solutions, it should not entirely replace manual coding. Developers must remain involved in the review and adjustment of generated code to ensure that project requirements are met accurately and efficiently.

The Role of Contextual Understanding

AI tools require a deep understanding of the programming context to produce meaningful results. This involves knowing data structures, algorithms, and best practices specific to the language and environment being used. Misunderstanding these elements can lead to incorrect or nonsensical code outputs.

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4.) Conclusion: A Step Forward with Caution

Using AI for generating parts of a game's backend logic was an interesting experiment that highlighted both its potential and limitations. For future projects, I plan to refine my prompts more effectively, balance automated generation with manual coding, and ensure that the context surrounding each prompt is well understood by the AI tool. By doing so, we can leverage AI as a powerful ally in our development arsenal without sacrificing the quality or control inherent in traditional programming practices.

In conclusion, while AI-powered code generation tools offer exciting possibilities for speeding up certain aspects of game development, they should be used with caution and alongside careful human oversight to ensure that projects align with specific needs and maintain high standards of functionality and robustness.

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The Autor: PixelSamurai / Takashi 2025-06-01