Flaky tests are one of the most corrosive problems a QA team can face: they erode trust in the entire suite, eat hours in re-runs, and tend to hide real bugs behind a...
Picture a typical LLM-as-judge setup: one model generates a response, and a second, different model grades it, a common safeguard meant to avoid the bias that creeps in...
Most LLM testing focuses on whether the answer is right. Almost nobody tests whether the output is structurally reliable: does it actually conform to the schema you...
Someone on your team drops a message: POST /api/checkout took 20,000 requests in a single hour. It reads like a performance bug, and it might be. But that number alone...
When teams build golden datasets to test AI decision-making systems, there's an assumption baked in from the start: that the data is fixed. A customer's order history, a...
Every so often a test fails with a diff you cannot see. The expected value and the actual value are printed one above the other, they are character for character the...
AI agents are increasingly used to write UI tests straight from a ticket description: give them the flow, point them at the frontend repo, and out comes a working...
Testing push notifications manually is tedious and error-prone. Every time you make a change to your notification logic, you need to trigger the notification, pull...
If your RAG system nails the first question but fumbles by turn three, you're not alone. While most testing focuses on retrieval accuracy and single-query...
You've just been asked to evaluate an AI model. Someone mentions it has "95% accuracy," and heads nod around the room. Sounds impressive, right? But here's...
