Perspectives

For years, QA practices were designed for predictable, rules-based software. AI has upended that reality by introducing risks that traditional methods cannot fully address.

We put two of the most talked-about models head-to-head in a real-world RAG scenario, and the results might surprise you.

AI is no longer just a technical feature, it is a business-critical system that shapes conversations, decisions, and customer experiences.

When you add AI to your product, the hardest part is not building the feature but making sure it works safely, reliably, and as intended in the real world.

Delivering analytics quality at a global scale is never easy. One broken event or missed signal can derail product launches, fuel bad decisions, and shatter customer trust overnight.

If you are building or scaling digital products, chances are your QA process already includes gig testers. You post a task, someone across the globe picks it up, files a bug, and moves on. 

AI failures rarely look like crashes. More often, they appear as confident but incorrect answers, subtle bias, or culturally inappropriate responses.

AI doesn’t just fail with bugs. It fails in silence, in bias, and in behavior. That’s why traditional QA won’t cut it anymore.

As software systems get increasingly complex every day, the challenges of effective testing also escalate. Software testing involves handling large datasets, complex workflows, and shorter release cycles.