Encountering coding errors in artificial intelligence (AI) projects can feel overwhelming, but a structured approach can transform the troubleshooting process into a manageable and efficient task.

In an AI-powered world where models learn, adapt and behave unpredictably, traditional monitoring capabilities are insufficient. If our applications are getting smarter, shouldn't our observability ...

AI-powered tools are integrated into everyday life. Our phones are the most obvious example; it's impossible to miss Gemini ...

It's 2025 and coding has entered its "just vibes" era. No more squinting at semicolons or manually debugging a rogue command chain, now you just tell an AI what you want and pray it doesn’t ...

A month after releasing the code for one of the core algorithms behind Bing, Microsoft Corp. today made another notable contribution to the open-source community. The company’s research division today ...

In 2020 a study showed the IT industry spent an estimated $2 trillion in software development associated with debugging code. The study also showed that 50 percent of IT budgets were allocated to ...

Despite Top Vole Sundar Pichai boasting that a quarter of Google's code now comes from AI and Mark Zuckerberg plotting to unleash AI models across Meta’s dev stack, Microsoft’s boffins have just ...

When an AI algorithm is deployed in the field and gives an unexpected result, it’s often not clear whether that result is correct. So what happened? Was it wrong? And if so, what caused the error?

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...