Let’s say you have to build a carousel component for an e-commerce site. How would you go about that?
One option would be to design it in Figma and then code it in HTML, CSS, and JavaScript. You’d have to create the layout, add navigation, implement transitions, and ensure responsiveness across different devices. Likely, you’ll also have to integrate with an e-commerce backend through an API and match the styling to your existing website.
With design, frontend, backend, and deployment, this quickly becomes a team endeavor.
Or, you could ask ChatGPT, “Can you create a carousel component for an e-commerce site?” and you’ll have the code in seconds. Now, it may not be a perfect technology quite yet, but it’s still a game changer for many developers.
No wonder more and more teams are electing for the latter option. According to the 2023 Stack Overflow Survey, “70% of all respondents are using or are planning to use AI tools in their development process this year. ” AI code generation is completely changing how development works. Here’s a rundown of AI code generation to give you an understanding of how it can fit into your organization.
What is AI Code Generation?
AI code generation refers to the process where artificial intelligence systems generate computer code to accomplish specific programming tasks. They do this by using advanced large language models (LLMs) to produce code in response to natural language prompts.
If we type “Can you create a carousel component for an e-commerce site?” into an AI service (OpenAI’s ChatGPT, in this case), the AI will respond with the code that best matches our request based on its training data and understanding of coding patterns.
Here is what was produced:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Product Carousel</title>
<link rel="stylesheet" href="styles.css">
</head>
<body>
<div class="carousel">
<button class="carousel-button prev" onclick="prevSlide()">❮</button>
<div class="carousel-track-container">
<ul class="carousel-track">
<li class="carousel-slide current-slide">
<img class="carousel-image" src="product1.jpg" alt="Product 1">
</li>
<li class="carousel-slide">
<img class="carousel-image" src="product2.jpg" alt="Product 2">
</li>
<li class="carousel-slide">
<img class="carousel-image" src="product3.jpg" alt="Product 3">
</li>
</ul>
</div>
<button class="carousel-button next" onclick="nextSlide()">❯</button>
</div>
<script src="script.js"></script>
</body>
</html>
This generated code aims to fulfill the specified requirements, including HTML structure, CSS styling, and JavaScript functionality for a fully interactive carousel component. The AI also produced the code for styles.css and script.js needed by this index.html. Let’s check it is working:
It does, with no changes bar adding an actual image.
Currently, there are three common ways developers might use AI code generation. The first is like above, directly from an AI chat interface, such as ChatGPT, Google Gemini, or Anthropic’s Claude. This approach is common when debugging current code or when the request is small, like a single component.
This first method is the most straightforward and accessible. It allows developers to quickly generate code snippets or solve specific problems without leaving their browsers or integrating new tools into their workflow. It's handy for rapid prototyping, exploring new ideas, or getting unstuck on tricky code. It works more like pair programming, where you have someone help you explore coding options. But, as it exists outside of a developer's usual workflow, it disrupts the flow and requires more manual copy and pasting to get the code to work.
The second is AI code generation services. These can be called via API, such as OpenAI Codex, or embedded directly into IDEs, such as GitHub’s Copilot (powered by Codex) within VS Code.
This approach offers better integration into a developer's workflow. It allows for real-time code suggestions and completions as you type, making it feel like a natural extension of the coding process.
This method allows developers to receive AI-generated code suggestions without switching contexts or interrupting their flow. It's great for:
Autocompleting repetitive code patterns
Suggesting function implementations based on comments or function names
Generating boilerplate code quickly
Offering alternative ways to solve a problem you're working on
This requires more setup, but the main advantages are the immediate feedback loop and the ability to iterate quickly on AI suggestions.
The third way developers use AI code generation is through specific tools for part of their workflow. These might be AI testing tools, development tools, or design tools that generate particular types of code to optimize parts of the development process. These tools integrate AI code generation into existing workflows without requiring developers to learn entirely new systems or significantly alter their current practices.
These specialized AI-powered tools can generate unit tests, create API endpoints, scaffold application structures, or translate design documents into functional code. By focusing on specific aspects of development, these tools offer targeted benefits while minimizing the learning curve. This approach allows teams to gradually incorporate AI assistance into their projects, picking and choosing where AI can provide the most value without overhauling their entire development methodology.
How AI Code Generation Works
At its core, AI code generation relies on LLMs trained on vast amounts of code from various sources. These models learn patterns, structures, and relationships within code across multiple programming languages and frameworks.
When a developer inputs a natural language prompt or a partially completed code snippet, the AI model processes this input through its neural network. Based on its training data and the context provided, it then predicts the most likely sequence of tokens (words, symbols, or code elements) that should follow.
The strength is in the model's ability to generalize from its training data. It's not simply regurgitating memorized code snippets but synthesizing new code based on learned patterns and the specific context provided.
For example, suppose you start typing a function definition in Python. In that case, the model recognizes the language and the function structure and can infer potential parameters and return types based on the function name and any docstring you've provided. It might suggest implementing common algorithms or design patterns that fit the context. As AI code generation technology evolves, we're seeing advancements like:
Multi-modal models that can understand both code and natural language explanations or even images of diagrams
Improved handling of project-specific conventions and styles
Better integration with version control systems and collaborative development environments
The Benefits of AI Code Generation
The main reason for using AI code generation is speed. Without having to type every line of code, the sheer velocity of code production means what might take a developer hours can be generated in seconds.
Beyond just speed, AI code generation helps developers with working in unfamiliar languages, building out boilerplate, integrating APIs, or using design patterns:
Unfamiliar languages: AI code generation works across multiple languages and frameworks, making polyglot programming easier. Developers can generate code in less familiar languages, reducing the learning curve and enabling faster cross-language development. This capability is particularly valuable for teams working on diverse tech stacks or when porting applications between different languages.
Boilerplate reduction: AI significantly reduces the tedium of writing repetitive boilerplate code, often necessary for setting up projects or implementing common structures. By generating this foundational code automatically, developers can focus on more complex and creative aspects of their projects. This saves time and reduces the likelihood of errors in these critical but often overlooked parts of a codebase.
Integrating APIs: AI code generation simplifies the process of integrating third-party APIs. It can generate client code, data models, and example API calls based on API specs or docs, reducing the time spent reading extensive documentation and writing structural code. This capability allows developers to quickly implement and test API integrations, accelerating the development of feature-rich applications.
Design patterns: AI code generation tools are adept at suggesting and implementing common design patterns, helping developers create more robust and maintainable code. By recognizing the context and requirements of a given task, AI can propose appropriate architectural patterns, ensuring that even less experienced developers can benefit from established best practices. This leads to more consistent, scalable, and efficient code across projects.
Because of all this, AI code generation has a huge cost-reduction benefit for businesses. By accelerating development cycles and automating routine coding tasks, companies can significantly reduce labor costs while maintaining or even increasing output. This efficiency allows teams to tackle more projects or features in less time, effectively doing more with fewer resources.
Challenges of AI Code Generation
The main concern for developers is the quality of AI output. Often, AI produces functional code that solves the immediate problem but lacks the nuanced architecture and optimizations that come from years of programming experience.
This can lead to issues with efficiency, scalability, and maintainability down the line, effectively introducing technical debt into a codebase. Research into “code churn,” the amount of code that needs to be changed or updated within two weeks, shows that these changes are increasing in frequency, suggesting that code quality is decreasing.
AI Code Generation
This can then negate the time saved in code production. Developers need to spend more time carefully reviewing and refining AI-generated code, as well as fixing bugs that are introduced into production.
Part of the quality issue can come from outdated training data. Software development is a fast-moving field, so the techniques used a few years ago that have become part of the vast datasets of existing code AI are trained on become obsolete. Newer libraries, API versions, or recent releases of languages may not be reflected in the AI's knowledge base. This can result in suboptimal code that doesn't leverage the latest best practices or features.
Developers relying too heavily on AI might use deprecated methods or miss out on more efficient solutions. They might also lose proficiency if they are overly reliant on AI and lack knowledge of the latest techniques and concepts.
Using AI Code Generation To Enhance Existing Workflows in Blackbird
With so many teams using AI, the tools underpinning this revolution will improve. Not only will you be able to generate any code on the fly, but you’ll also be able to use these tools to increase developer productivity in specific workflow elements.
API development is one such area where this enhancement is happening. Without AI, teams are left to manually mock API endpoints, create documentation, and debug API errors. With AI, API development becomes a streamlined and efficient process. Tools like our new API development platform, Blackbird, combine the power of AI with expertise in multi-cluster and cloud-native tools to offer a cloud and CLI-accessible platform that simplifies and accelerates API development.
Using AI-powered elements in your development and combining that with a platform that helps organize and orchestrate your API development lifecycle is the ultimate winning combination.
Instant API design and coding: AI can quickly generate API structures and code based on your specifications, saving hours of manual work.
Fast and easy API mocking: Create realistic API mocks in seconds, allowing for rapid prototyping and testing without waiting for backend implementation.
Advanced API testing and debugging: AI can generate comprehensive test cases, simulate various scenarios, and help identify potential issues before they reach production.
Streamlined documentation: Automatically generate and update API documentation, ensuring it syncs with your actual API implementation.
By leveraging these AI-enhanced tools, development teams can produce high-quality APIs faster and simplify API management across their organization. This accelerates the development process and allows developers to focus on more complex, value-adding tasks rather than getting bogged down in repetitive API-related work.
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