SE-Mentor brings a practitioner’s perspective to quality engineering, sharing insights grounded in real-world execution. These perspectives help technology leaders make sharper decisions, reduce risk, and accelerate outcomes.
Building QA Frameworks for Generative AI Applications: Using AI to test AI Testing a Generative AI application is like coaching a student rather than checking a calculator. With traditional software...
Testing AI Systems: Beyond Accuracy to Trust, Bias, and Explainability Testing AI systems has shifted from a simple “pass/fail” grade to a much deeper evaluation. While accuracy—how often...
The Vibe Revolution: Code Faster, Build Smarter, Secure Your Success The moment “Vibe Coding” entered our vocabulary, it signaled the end of development drudgery. It’s the ultimate...
Self refining AI Feedback Loop: Continuous Learning Systems in Production There was a time when launching software was like crossing a finish line—you finish the work, ship it, and you’re done...
Data Readiness for AI: The Hidden Challenge Behind Successful Models Most people think building a great AI is about the “brain” (the model). But in reality, the biggest challenge is the...
From Data to Deployment discipline: Mastering the AI Lifecycle Building a modern AI system is like setting up an automated factory. In the past, engineers spent all their time building the...
Keeping AI Honest: Why Observability Matters More Than Ever Monitoring AI in the real world is different from monitoring regular software. With a normal app, “broken” usually means the app...
MLOps vs LLMOps: The Operational Shift defining Modern AI To understand the difference between MLOps and LLMOps, think of it as the difference between running a high-speed calculator and managing a...
Human-in-the-Loop: The missing Layer That Makes AI Trustworthy Imagine a high-speed train hurtling down a track. It is efficient, tireless, and capable of speeds no human could sustain. But...
