Ever typed a garbled search and gotten bizarre results? We all have. Typos, voice input errors, or even just not knowing the right terminology can seriously mess with search engines. That's why query correction is so vital—it’s the behind-the-scenes magic that tries to understand what you *meant* to search, even when you didn't type it perfectly. Traditional correction models are like spellcheck on steroids. They’re good at catching simple errors but struggle with context. Large Language Models (LLMs), on the other hand, are great with context but can be slow and costly to run, plus they sometimes ‘over-correct’, changing things you actually got right. So, researchers developed a clever framework called Trigger³ that combines the strengths of both. Trigger³ acts like a triage system for search queries. First, it checks if the query even needs correcting. If it does, it uses a small, efficient model to try a quick fix. If that doesn’t work, a second ‘trigger’ decides whether to call in the LLM heavyweight. Finally, a third ‘fallback trigger’ checks if *both* models messed up and decides whether to simply use the original query after all. This multi-stage approach is not just about fixing typos; it's about making search smarter. Trigger³ improves accuracy by selectively applying the right level of correction, while maintaining efficiency by only calling on the power-hungry LLMs when absolutely needed. The tests on real-world search data show that Trigger³ outperforms other methods, boosting accuracy without sacrificing speed. This kind of research is paving the way for even more intuitive and powerful search experiences, where the search engine truly understands what you’re looking for, no matter how you type it.