Author name: Megha Chouhan

Improving AI reliability requires moving beyond one-time evaluation and building systems that continuously monitor, validate, and refine outputs in real-world […]

AI systems fail in domain-specific tasks because general-purpose models are not trained with deep expertise required for specialized fields like

There is no single ground truth in AI evaluation because many AI and LLMs tasks do not have one correct

LLMs give confident but wrong answers because they are designed to generate fluent and coherent responses, not to verify accuracy

Prompt engineering does not solve AI reliability because it only influences how a model responds, it does not change how

AI outputs fail in edge cases because models are trained mostly on common patterns, not rare or unusual scenarios. When

AI systems lack consistency because they generate responses probabilistically rather than deterministically. This means the same input can produce different

AI systems often perform well in testing environments but fail in production because real-world inputs are far more complex, unpredictable,

LLM benchmarks are unreliable because they measure performance on static datasets that do not reflect real-world complexity. While benchmarks provide