Blogs

If you’ve been following the AI space, you’ve likely heard the buzz around evals. Industry leaders agree that evaluating LLMs is key to building effective AI products. As AI continues to grow, learning how to run evals will become a must-have skill for QA professionals, product managers, and developers

The "Ghibli Trend" wasn't just another online craze — it became a live performance and load-testing scenario for OpenAI. Social media users began sharing Ghibli-inspired AI images, creating a massive buzz.

Generative AI applications such as ChatGPT excel at making conversations, creating jokes, writing texts, and providing programming assistance, but how do we make sure they’re smart, safe, and fair?

Artificial intelligence (AI) has become an integral part of modern software, driving innovation across industries from healthcare to finance. However, as AI systems increasingly influence critical decisions, concerns around bias, fairness, and ethical implications have come to the forefront. Quality Assurance (QA) professionals are uniquely positioned to address these challenges, ensuring that AI systems are not only functional but also equitable and ethical.

An AI testing tool includes machine learning (ML), natural processing language (NLP), and predictive analysis that helps in improving testing methodologies. This tool simulates a human tester, allowing for faster testing, smart identification, and automation that continues to learn how to avoid false positives over time.

Some of the popular AI tools people and corporations are using now include ChatGPT, Google Gemini, and Microsoft Copilot. This has resulted in higher usage and adoption of this technology and this has caused some worry among people, particularly in terms of employment.