As AI becomes smarter, it is easy to assume machines can work entirely on their own. But in reality, even the best models need a human touch. That’s where Human in the Loop (HITL) comes in. It is a process where humans actively guide, review and fine tune the AI, especially in situations where data alone isn’t enough. For example, when an AI model gives a result with low confidence, a human steps in to verify or correct it. This ensures decisions are accurate, ethical and aligned with real world expectations. Whether it’s moderating content, approving financial risks, or reviewing healthcare predictions, human oversight act as a safety net. As a QA, I see HITL not as a fallback, but as a vital part of building trust and reliability into any AI system.

One common challenge in AI is, it learns from the past data only but the real world keeps changing. What happens when the AI faces a new or rare situation it wasn’t trained for? That’s when HITL really proves the value. Humans can catch edge cases, provide empathy in customer support and reset model accuracy when things drift. In fields like law enforcement or healthcare, the cost of a mistake is high, so always relying solely on automation isn’t enough. Human judgement is important and it fills the gap between what AI knows and what it still needs to learn. And as AI continuous to evolve, Human in the loop will remain key in training, testing and improving models and also ensuring we don’t just build smart systems, but responsible one too.