Why LLM's cant do s##t ?

We know the title grabbed your attention, and that’s the point. Stick with us, and we’ll explain why it’s not as harsh as it seems.

LLMs excel at language tasks but often fall short in complex reasoning. While powerful, they can't read minds or replace human judgment. The key to effective LLM-powered applications is having realistic expectations and focusing on human-AI collaboration.

But here’s a controversial thought: can a system truly learn reasoning just by consuming massive amounts of 'reasonable' arguments found online? The jury’s still out on that one.

Some argue that the problem is simply a matter of tokenization, but what if the real issue is that no one truly knows how to build reasoning systems? This sobering reality highlights the importance of being realistic about what LLMs can and can't do when developing AI-powered products. To get the most out of these mysterious systems, we must implement control mechanisms and safety nets. Yet, it begs the question: are we simply restraining something we don’t yet understand? As we push the boundaries of AI, the line between extending its capabilities and admitting its limitations is increasingly unclear.

A parallel can be drawn to the development of self-driving cars. Despite years of progress, fully autonomous vehicles remain elusive, with the automotive industry finding success by focusing on assisted driving features that enhance human capabilities rather than replacing them entirely. Similarly, in the AI landscape, LLM-powered applications that prioritize human-in-the-loop approaches are set to lead the race. These systems leverage AI’s strengths while acknowledging the irreplaceable value of human judgment, creativity, and context understanding. By combining AI assistance with human expertise, we can achieve more practical, immediate benefits—much like how advanced driver-assistance systems have greatly improved road safety without fully automating the driving experience.

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