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Stop me if you’ve heard this one before: You’re staring at a blank code editor, dreading the repetitive boilerplate code you’ll need to write, or stuck trying to untangle a particularly tricky bug. You’ve heard about generative AI tools that can write code, but here’s the burning question—can you really trust them to get the job done?
Generative AI models like GitHub Copilot and ChatGPT have been making waves in the tech world. They promise to streamline workflows, save time, and even spark developers’ creativity.
But with every promise comes scepticism:
- How reliable is AI-generated code?
- Will it work for my project?
- And can it really replace—or complement—human expertise?
In this article, you’ll learn how generative AI models can assist with coding, what they’re best at, and where they might fall short.
What is Generative AI for Coding?
Generative AI for coding refers to artificial intelligence models designed to assist developers by writing, completing, or debugging code. These models are powered by advanced machine learning techniques, particularly natural language processing (NLP) and deep learning, enabling them to understand programming languages much like how they process human languages.
At their core, these tools have been trained on massive datasets of existing code repositories, technical documentation, and other programming-related resources.
The result? They can predict and generate code snippets, suggest solutions, or even create entire functions based on a user’s input.
For example, you might describe a function you need— “Create a Python function to calculate the factorial of a number”—and a tool like ChatGPT or GitHub Copilot could generate the corresponding code.
How Do They Work?
Here’s a simple example of how it works:
- Input Processing: The user provides a prompt, which could be a description of a task, a partially written code block, or even a query about debugging an issue
- Code Generation: The AI processes the input and generates suggestions or complete solutions by drawing on patterns it learned from its training data
- Feedback Loop: Users can review, modify, or refine the AI-generated code, creating a collaborative cycle that combines human creativity with machine efficiency
Where Do They Shine?
- Boilerplate Code: Automating repetitive coding tasks like setting up APIs or database schemas
- Code Completion: Predicting and completing code based on context, like how autocomplete works
- Debugging Assistance: Helping identify errors or offering fixes for common coding mistakes
What Can Generative AI Models Do?
Generative AI models have unlocked a new level of productivity and creativity for developers, but their capabilities aren’t one-size-fits-all. Here’s what these tools can do exceptionally well:
1. Write Boilerplate Code
One of the most tedious tasks in programming is creating boilerplate code—those repetitive, foundational pieces that are necessary but uninspiring to write.
Generative AI models excel here by quickly producing scaffolding for projects, such as:
- Setting up REST APIs
- Creating database models
- Writing configuration files
This automation frees up your time for more complex or creative aspects of coding.
2. Generate Code from Descriptions
Imagine describing what you need in plain language, and the AI does the heavy lifting. For someone who isn’t an expert in any coding languages, this is a gamechanger.
Rather than having to go to a developer for everything, AI can probably solve your problem some of the time.
Here’s an example in action:
3. Provide Code Suggestions and Autocompletion
Tools like GitHub Copilot act as intelligent pair programmers, offering real-time code suggestions as you type. For instance, as you begin writing a loop, the AI might suggest the entire structure based on context. This can save significant time if you’re working on a project.
4. Debugging and Error Resolution
Generative AI can help identify and fix errors in your code. By pasting an error message or snippet into an AI tool, you might get:
- An explanation of what’s wrong
- Suggested fixes tailored to your code
- Insights into why the issue occurred, which can help prevent future mistakes
5. Translate Between Programming Languages
Generative AI can convert code from one language to another. This is particularly handy when porting a project or collaborating with a team that uses different technologies. For instance, you can ask it to translate Python code into JavaScript or vice versa.
Limitations & Risks of AI for Coding
Generative AI models bring a wealth of possibilities to coding, but they are not without their challenges. Understanding their limitations and the risks involved is crucial for using them responsibly.
1. Inaccuracy in Complex Scenarios
While AI excels at generating straightforward code, it struggles with:
- Highly complex logic: AI might produce code that looks correct but fails under specific conditions. Imagine you’re building a stock trading algorithm that factors in historical data, real-time market trends, and predictive analytics. If you ask an AI to create this, it might produce an incomplete or oversimplified solution that doesn’t address edge cases or your specific business requirements
- Context-sensitive tasks: AI might fail when tasked with creating a machine learning pipeline tailored to your custom dataset. While it might suggest general code for loading and training a model, it won’t understand the intricacies of your dataset’s structure, or the nuances of feature engineering needed for optimal results. For example, asking an AI to create an algorithm for a unique business process may result in something that requires significant manual tweaking to be usable
2. Lack of Understanding
Generative AI doesn’t “understand” code the way humans do. It generates outputs based on patterns it learned from data, which can lead to inaccurate results and overconfidence in errors.
For example, imagine you’re asking an AI tool to generate a headline for a Facebook ad campaign. You type: “Write me a headline about a sale.”
Without enough context, the AI might generate something like:
“Get Great Deals Before They’re Gone!”
While this is a generic headline, it doesn’t reflect the specific sale, target audience, or unique value proposition. It might fail to engage your audience because it’s too vague.
Similarly with code, if you don’t give the AI enough context (and sometimes, even if you do), it can misunderstand the purpose of the code, which can lead to an output that doesn’t match what you’re trying to achieve.
3. Security Risks
AI-generated code can inadvertently introduce vulnerabilities. For example, asking AI to generate an authentication system might result in hardcoded passwords or improper encryption practices that could be exploited by attackers.
On top of that, models trained on older datasets may suggest insecure or outdated techniques.
Relying solely on AI without reviewing its output could expose you to attacks.
4. Ethical and Legal Concerns
Generative AI models are trained on publicly available code, which may include:
- Copyrighted material: Using AI-generated code might inadvertently violate intellectual property laws. For instance, asking for a function similar to one from a popular library might generate something almost identical, potentially breaching copyright laws
- Open-source license conflicts: AI could produce code governed by licenses like GPL, requiring you to adhere to specific terms. For example, suppose the AI generates a snippet inspired by a GPL-licensed project. If you include this in a proprietary application, you may unknowingly violate the license terms
5. Limited Support for Niche or Emerging Technologies
AI performs best with well-documented, widely used programming languages and frameworks. If you’re working with cutting-edge or niche tools, their usefulness may be limited.
How to Mitigate These Risks
The main way to mitigate these risks is to treat AI-generated code as a starting point, not a finished product.
Use AI to speed things up and get the brunt work done, but make sure there is someone reviewing everything before it’s being used.
You might say, “What’s the point in using AI for code generation if we still need humans?”
Well, if AI can do most of the work, the time saved can mean that you may spend fewer resources on any particular project. It also means that your developers can spend more time focusing on thinking and solving problems than on doing the grunt work.
Tip: Think of AI as a junior developer—it can save time but still needs guidance and supervision.
All in all, using AI for code generating can speed things up, but you have to be aware of the risks and mitigate them effectively.
Popular Tools for AI Code Generation
The rise of generative AI has brought an array of tools designed to help developers write, debug, and optimise code. Each tool offers unique features and excels in different scenarios. Here’s a look at some of the most popular options:
1. GitHub Copilot
Overview: GitHub Copilot, developed by OpenAI and GitHub, acts as an AI-powered pair programmer. It integrates directly with your IDE and offers code suggestions as you type.
Key Features:
- Contextual code completion and suggestions
- Autogeneration of boilerplate code
- Support for multiple programming languages, including Python, JavaScript, and Go
Best For: Developers looking for real-time assistance while coding in supported IDEs like Visual Studio Code.
Limitations:
- May struggle with highly specific or niche tasks
- Suggestions may sometimes include redundant or overly verbose code
2. ChatGPT (OpenAI)
Overview: ChatGPT is a conversational AI model capable of understanding and generating code based on prompts. Unlike GitHub Copilot, it operates in a chat-based interface rather than integrating directly into an IDE.
Key Features:
- Generates complete code snippets based on detailed descriptions
- Debugs existing code by analysing error messages or issues
- Explains code concepts, making it a great learning tool
Best For: Marketers or non-developers looking to work with code
Limitations:
- Requires copy-pasting between the chat interface and your IDE
- Struggles with large, highly interconnected codebases without sufficient context
3. Amazon CodeWhisperer
Overview: Amazon’s AI coding assistant is designed for developers working within AWS environments. It provides code suggestions tailored to AWS services and infrastructure.
Key Features:
- Deep integration with AWS, offering optimised code for Lambda, S3, and other services
- Real-time suggestions and autocompletion
- Security scanning for vulnerabilities in generated code
Best For: Developers building applications on AWS who want AI assistance optimised for the ecosystem.
Limitations:
- Limited appeal outside the AWS ecosystem
- Can be less versatile than tools like Copilot for general-purpose coding
4. Tabnine
Overview: Tabnine is an AI-powered autocompletion tool that integrates with a variety of IDEs. It uses your codebase to provide tailored suggestions.
Key Features:
- Personalised code suggestions based on your coding patterns
- Supports a wide range of programming languages
- Works offline, ensuring data privacy for enterprise users
Best For: Teams or developers prioritising privacy and customisation.
Limitations:
- Less conversational than ChatGPT or Copilot, focusing more on autocompletion
- Requires some configuration to maximise its effectiveness
5. IntelliCode (Microsoft)
Overview: IntelliCode enhances your coding experience within Visual Studio by providing intelligent suggestions based on your code context and best practices from open-source projects.
Key Features:
- Recommendations tailored to your coding habits
- Team-focused features like shared coding standards
Best For: Developers working in Microsoft’s ecosystem who want lightweight AI assistance.
Limitations:
- Focused on improving existing workflows rather than generating large code blocks
How to Choose the Right Tool
- For Real-Time Assistance: Choose GitHub Copilot or Tabnine for in-editor suggestions
- For Marketers and/or non-developers: Use ChatGPT for interactive, conversational support
- For AWS-Specific Projects: Go with Amazon CodeWhisperer for AWS-optimised suggestions
- For Privacy-Focused Teams: Tabnine offers offline functionality and customisation
- For a Versatile AI Backend: Codex provides flexibility for diverse programming needs
By selecting the right tool for your goals and understanding their strengths and limitations, you can seamlessly integrate AI into your development workflow.
The Biggest Lesson… Don’t Be Left Behind
Generative AI is powerful. And if you’re not using it, you’re going to be left behind. Seriously.
One of the biggest advantages we’ve experienced with AI (which has saved us significant resources) is the creation and use of custom ChatGPT bots.
These allow you to achieve specific outcomes relevant to your use case scenario.
Learn how to create a custom ChatGPT bot and automate your workflow in this article.
