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Your AI chatbot might give a customer the wrong price. A RAG-based support agent might cite a document that doesn’t exist. An AI coding assistant might suggest code with a security problem. These issues are common for teams releasing LLM features without proper testing. The reality is that many teams using GPT, Claude, or Gemini don’t have a strong testing strategy. They usually do a few manual checks or simple prompt tests and assume it’s enough.

Over the last year, at Testlio, we have upskilled more than 600 testers in our global community to test AI-powered applications.

February introduces practical improvements across Test management, TestCloud, and TrueTest that help teams move faster while maintaining better control over testing operations. Here's what we have released.

Recent outages tied to AI-assisted code changes have pushed companies into a corner. After several incidents with massive “blast radius” impacts, organizations like Amazon introduced stricter controls—mandating that senior engineers manually review all AI-generated code before it hits production. That response makes sense on paper, but it exposes a fatal flaw in the modern development pipeline.

Regression testing is a software testing process that ensures your existing features, designs, and dependencies continue to work as expected after changes or updates are made to your codebase. It detects unintended bugs or breaks introduced by modifications like new features, bug fixes, or configuration changes. Each new change introduces a risk of breaking existing functionality, potentially causing shipping delays or launch postponements.

A $25 billion Fortune 500 medical device company, Stryker, was targeted by an Iran-linked hacker group that claimed to have wiped over 200,000 servers, mobile devices, and other systems, forcing the company to shut down offices in 79 countries. The medical technology industry has been hit hard by this huge problem. It's a stark warning that even the largest names in the business world can be hit by clever wiper malware.

Every QA in global releases tells two stories: the one the teams plan for and the one users experience. And often, those stories don’t match.

Data masking vs. tokenization — which should your organization be using to protect sensitive data? The simplest answer: if you need to easily re-access original data, tokenization is preferable. If you need irreversibly transformed data for development or analytics, masking is the superior choice. This is especially true when it comes to using data for artificial intelligence (AI).

The promise of test automation was simple: automate repetitive testing tasks, catch bugs faster, and ship quality software at scale. Yet for most development teams, that promise remains unfulfilled. Traditional test automation frameworks demand specialized coding skills, require constant maintenance when applications change, and create bottlenecks that slow down release cycles rather than accelerate them.

When a login button breaks, you know it. The bug is reproducible, the fix is straightforward, and the path from problem to resolution is clear.