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  • Maryam Hassani

Don't Get Left Behind: A Guide to Using AI Code Generation in Your Business

Updated: Apr 14

AI delivers impressive speed gains for developers writing new code, optimizing existing code, translating code from one coding language to another, and so on. In fact, you don’t even need to code at all…

By simply typing in the human language, AI code generation tools let you generate scripts in Java, Python, Ruby, and COBOL, you name it. All you need is to prompt it, right? Technically, yes. You can ask several generative AI models to write the required code — but can you check if it’s correct?

AI code generation is a brilliant tool for software developers to speed up the process of updating legacy code and building new projects. However, users must understand the foundation of coding, computer infrastructure, and organizational objectives to maximize its functionality. 

With that in mind, let's look at some exciting benefits for companies, what to look out for, and how you could apply them in your company.

What is Coding With AI?

AI code generation can understand and produce syntax, patterns, and paradigms across numerous coding languages. To do this, it leverages a combination of natural language processing (NLP), deep learning algorithms, and, most importantly, large language models (LLMs).

Developers train LLMs on publicly available open-source code or fine-tune them with proprietary code — although, the more diverse the data, the better. This training allows the tools to identify the various coding language nuances, giving it the power to respond to different prompts. It can provide anything from code snippets, i.e., individual building blocks, to complete functions of code, which assemble these blocks to achieve a more comprehensive functionality.

How Businesses Can Win

For experienced developers, writing code with AI takes nearly half the time, McKinsey reports. And the best part is that AI-assisted code even improves code quality, particularly in terms of bugs, maintainability, and readability (which contributes to reusability).

For example, you want to personalize your e-commerce website by dynamically recommending outfits to customers based on browsing history and past purchases. Instead of laboriously writing code — that analyzes customer data, retrieves relevant product information, and assembles outfit recommendations with logic rules — developers can:

  1. Upload the data: Provide the AI code generation tool with data on customer product attributes (color, style, size), and any existing rules for outfit combinations with some code so the AI knows the context.

  2. Generate code: Prompt the AI to create code that will provide the requested effect, and in a format that can be copied and pasted. 

  3. Test, refine, and integrate: Give the code a go to see if it does what you asked it to, refine the generated code or prompts if necessary, and integrate it into the website.

By generating code with AI, talented programmers save tons of time, meaning they can prioritize governing, refining, and eliminating errors in outcomes.

Be Mindful of the Speed Bumps

Reaping the benefits of AI code generation tools really depends on the user’s existing knowledge of particular programming frameworks and the complexity of the task at hand. The McKinsey report found, in some cases, tasks took junior developers 7 to 10% longer with the tools than without them.

Let's say a coding novice uses AI code generator tools like Devin AI to integrate a chatbot into your finance app. However, the developer still needs to understand the code generated using AI. Without understanding, they couldn't check and correct what AI generates — and that may produce security risks. AI may misunderstand the task and develop something other than what is required. For that reason, checking and fixing without understanding the basics may not be effective.

Getting products from a polished demo to something useful in the real world is no walk in the park. Old software, business quirks, and tons of integrations require human intelligence and wisdom to understand how to optimize code within the current ecosystem — even if you can ask AI code generation to help you with it.

What's Next? The Future of AI Coding

AI code generation is changing the game for developers when it comes to code creation, translation, optimization, and debugging. With talented programmers in the driver's seat, doors open to self-learning code that requires continually less prompting and can complete multiple functions after each other. AI code generation automates code creation based on existing knowledge, while self-learning code aspires to learn and improve the code itself over time. (Like an apprentice programmer who learns from examples and experience, gradually enhancing their coding skills!)

The idea is that future developers can get a good night's rest, wake up to hours' worth of coding done, and be ready with their governing eye. Not only does this boost coders’ efficiency, but it's also bringing everything in the tech space up to a certain standard — allowing talented programmers to shine. 

Introducing Zaia: Built With Self-Learning Code

Our product, Zaia, is a perfect use case. Frank, our CTO and co-founder, has developed a bespoke code generation AI system, that we have already used to build Zaia — a networking tool that allows professionals to collect and action connections at events seamlessly using just their LinkedIn URL.

By scanning one another’s LinkedIn QR codes or business cards at events, users can instantly connect with fellow attendees. The tool’s intelligent algorithms are busy away in the background, suggesting ways to collaborate so users can build relationships meaningfully — all while they share their first sip of coffee. The user can add personal notes for each contact, and Zaia also sends reminders on action points to help with follow-ups.

The background code generation system is explicitly designed to build its own code — its own components. When prompted to build the Zaia app, for example, it could use, reuse, or share previously generated components which would become a part of the main system, without generating parts again. The system is optimized for building simple to big apps that are stable and are built in collaboration with the programmer.

Simply put, AI code generation isn't coming for your jobs; it's enhancing them. Developers can boost productivity, enrich code quality, and eliminate bugs by upskilling with these tools. As long as coders keep their skill sets sharp and are up to date with coding advancements, they will stay competitive.

Interested in what’s possible with AI code generation? Try Zaia at your next business event and see for yourself!

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