<p>Welcome to <a href="https://oski.solutions/" rel="noopener noreferrer" target="_blank">OSKI Solutions</a> Talks, where we discuss software development, AI, and the challenges of building technology that works. Today, we're talking about something that's changing the way software gets built: AI-generated code. You've probably seen those videos where someone describes an app in a few sentences, clicks a button, and suddenly there's working software on the screen. Pretty impressive, right? AI can write functions, suggest fixes, generate tests, and handle tasks that used to take developers hours. It's becoming part of everyday software development, and for good reason. But here's the question: what happens when the code looks right but isn't? That's the tricky part. AI-generated code doesn't always fail in obvious ways. Sometimes everything works until a customer does something unexpected. Sometimes a calculation is slightly wrong. And sometimes code that runs perfectly in a demo starts causing problems when real people use it. Now imagine that happening in a system managing orders, financial data, or inventory. Suddenly, a small mistake can become a much bigger problem. So how do we know when AI-generated code is actually reliable? The answer starts with testing. Developers don't just write code and hope it works. They review it, test different scenarios, and check how it interacts with the rest of the system. AI-generated code needs exactly the same attention. One approach is to test the generated code against the actual system where it's supposed to run. If something fails, the system can identify the error and give AI another chance to correct it. And this isn't just a theoretical idea. OSKI Solutions worked with Lokad, a supply chain software company, on an AI assistant that generates scripts in a specialized programming language called Envision. Instead of simply showing users whatever AI produces, the assistant checks those scripts against the real execution engine. If something goes wrong, the error can be used to improve the result. It's a practical example of how AI becomes more useful when there's a way to verify its work. But here's another important detail. Code can run without errors and still do the wrong thing. Imagine asking AI to calculate a customer discount. The code might work perfectly, but what if it applies that discount to the wrong products? Technically, the code runs. From a business perspective, there's a problem. That's why human judgment still matters. Developers understand business requirements, consider security risks, and make decisions about how different systems should work together. AI can help them move faster, but it doesn't automatically understand every business rule or recognize every potential issue. And that's worth remembering when people talk about AI replacing developers. Writing code is only one part of software engineering. Making sure the software is reliable, secure, and useful is just as important. So, can you really trust AI-generated code? Yes, but not without checking it first. AI can save time and make development more efficient, but the real value comes from combining that speed with proper testing, validation, and experienced engineers. Because at the end of the day, it doesn't matter how quickly the code was written if it doesn't do what it's supposed to do. Thanks for listening to OSKI Solutions Talks. If you'd like to continue the conversation about AI, software development, or building reliable business applications, feel free to get in touch with our team via <a href="hello@oski.solutions" rel="noopener noreferrer" target="_blank">email</a>.</p>

Welcome to OSKI Solutions Talks, where we discuss software development, AI, and the challenges of building technology that works. Today, we're talking about something that's changing the way software gets built: AI-generated code. You've probably seen those videos where someone describes an app in a few sentences, clicks a button, and suddenly there's working software on the screen. Pretty impressive, right? AI can write functions, suggest fixes, generate tests, and handle tasks that used to take developers hours. It's becoming part of everyday software development, and for good reason. But here's the question: what happens when the code looks right but isn't? That's the tricky part. AI-generated code doesn't always fail in obvious ways. Sometimes everything works until a customer does something unexpected. Sometimes a calculation is slightly wrong. And sometimes code that runs perfectly in a demo starts causing problems when real people use it. Now imagine that happening in a system managing orders, financial data, or inventory. Suddenly, a small mistake can become a much bigger problem. So how do we know when AI-generated code is actually reliable? The answer starts with testing. Developers don't just write code and hope it works. They review it, test different scenarios, and check how it interacts with the rest of the system. AI-generated code needs exactly the same attention. One approach is to test the generated code against the actual system where it's supposed to run. If something fails, the system can identify the error and give AI another chance to correct it. And this isn't just a theoretical idea. OSKI Solutions worked with Lokad, a supply chain software company, on an AI assistant that generates scripts in a specialized programming language called Envision. Instead of simply showing users whatever AI produces, the assistant checks those scripts against the real execution engine. If something goes wrong, the error can be used to improve the result. It's a practical example of how AI becomes more useful when there's a way to verify its work. But here's another important detail. Code can run without errors and still do the wrong thing. Imagine asking AI to calculate a customer discount. The code might work perfectly, but what if it applies that discount to the wrong products? Technically, the code runs. From a business perspective, there's a problem. That's why human judgment still matters. Developers understand business requirements, consider security risks, and make decisions about how different systems should work together. AI can help them move faster, but it doesn't automatically understand every business rule or recognize every potential issue. And that's worth remembering when people talk about AI replacing developers. Writing code is only one part of software engineering. Making sure the software is reliable, secure, and useful is just as important. So, can you really trust AI-generated code? Yes, but not without checking it first. AI can save time and make development more efficient, but the real value comes from combining that speed with proper testing, validation, and experienced engineers. Because at the end of the day, it doesn't matter how quickly the code was written if it doesn't do what it's supposed to do. Thanks for listening to OSKI Solutions Talks. If you'd like to continue the conversation about AI, software development, or building reliable business applications, feel free to get in touch with our team via email.

OSKI Solutions Talks

Can You Really Trust AI-Generated Code?

<p><a href="https://oski.solutions/" rel="noopener noreferrer" target="_blank">OSKI Solutions</a> Talks explores software development, artificial intelligence, and the technology behind modern businesses. We discuss custom software, AI integrations, business automation, CRM and ERP systems, cloud solutions, and the challenges of building and maintaining reliable applications. From choosing the right technologies to modernizing existing systems, we share practical insights into software engineering and how businesses can use technology to solve real problems.</p>

OSKI Solutions Talks explores software development, artificial intelligence, and the technology behind modern businesses. We discuss custom software, AI integrations, business automation, CRM and ERP systems, cloud solutions, and the challenges of building and maintaining reliable applications. From choosing the right technologies to modernizing existing systems, we share practical insights into software engineering and how businesses can use technology to solve real problems.

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Can You Really Trust AI-Generated Code?