Model Reliability

Why this matters now Modern AI models are astonishingly good at pattern recognition—and dangerously bad at knowing which patterns matter. A neural network that labels birds can achieve 95% accuracy on paper yet collapse when the background changes from lake to desert. This fragility stems from spurious correlations—the model’s habit of linking labels to irrelevant cues like color, lighting, or background texture. The deeper the network, the deeper the bias embeds. ...

“Fine-tuning and prompting don’t just teach—sometimes, they mislead. The key is knowing how much to trust new information.” — Cognaptus Insights 🧠 Introduction: When Models Learn Too Eagerly In the world of Large Language Models (LLMs), LoRA fine-tuning and prompt engineering are popular tools to customize model behavior. They are efficient, modular, and increasingly accessible. However, in many practical scenarios—especially outside elite research labs—there remains a challenge: Enterprise-grade LLM deployments and user-facing fine-tuning workflows often lack structured, scalable mechanisms to handle input quality, model confidence, and uncertainty propagation. ...