Are LLMs the Solution to Every Problem? | AI Explained

Key Takeaways

In the late 2022, the world saw the introduction of OpenAI's new software called Chat GPT. >> Chat GPT Chat GPT Chat GPT and it changed the world that we see today. This particular tool could write emails for us, solve complex mathematical problems, and even generate audios and videos that would take humans nearly days or even weeks to accomplish. Now due to its so powerful ability, not only its users which were merely students or even working professionals, big tech AI firms started to use this particular tool in their workforce. Interested in increasing their productivity, many big tech firms wanted to increase the usage of AI in their workforce or even create their own AI tools. But by 2026, the story has changed a lot. Many companies have reported a huge increase in their expenditure in this AI tools because of which they have cut down their profits leading to an ultimate loss. Many startups that were completely based on these AI solutions have either closed or on the verge of bankruptcy. Hi, this is Jen and today we are going to uncover this fact that is LLM the answer to every problem. Now before deep diving into this topic, let's just understand what do we mean by the term LLM. Now see any tool that we see in this world such as tag GPT, Gemini or even the claude model of entropic these all models are LLMs or we can say large language models. Now large language models are specialized neural networks that have been trained on massive amounts of text data so that they are able to predict what is going to be the next word in a sentence. Now, due to this incredible ability that these LLMs have because of their training, we are able to construct emails from scratch, even create stories, write full-fledged articles that would take us minutes or even let's just say hours to accomplish. Not only this, LLM could create personalized answer for us because they understood the context behind our problems. Now due to this astonishingly highly used ability to understand some context, people started using LLM for various tasks into day-to-day lives. Now students use these LLMs to create answers to their homeworks. Uh coders use these LLMs to create code snippets and even famous artists use these LLMs to generate art which would take them hours or even days to accomplish. So now one could argue that why not use LLM to solve each and every problem that we have. Why not include LLM in every particular domain that we are currently having to increase its productivity and even boost some of the task that would take a lot of human workforce. But that is why we need to understand why it cannot be done. Now [snorts] see these LLMs are probabilistic in nature meaning that they are not knowing the fact that if the answer is wrong or right. They are only working on the basis of probability meaning they are guessing what the correct answer could be. Therefore sectors such as banking, finance, legal advices, medical domains etc etc require high level precision and there the guesswork could not work. Just take a look at this particular example. This particular airline was using LLM for answering the user's query and this LLM hallucinated or you can say guessed what could be the perfect answer but it was not factually correct and they were held responsible for the error caused by their LLM or you can say the AI tools that were using. Another big reason is cost. See LLMs are very costly in nature. Now it would make sense that if I was to use an LLM in a place where a simple 10line Python script or even a SQL query would work is a good practice. Now why is that? Let's just imagine a situation. For example, you want to [snorts] buy milk from the nearest grocery store possible which is let's just say 100 to 150 m from your house. Now there are two options. Either you could take a very fuel hungry massive truck or a very simple bicycle. Now see both the vehicles are going to get you to that location but using the very heavy fuel hungry massive truck would cost you a lot of money whereas the simple bicycle could work for let's just say virtually free therefore since it was such a powerful tool many companies were blindly using LLM in places where it was not even needed and therefore because of this overuse of LLMs they have reported heavy losses in their revenue. Another big reason is that we do not require LLM to solve each and every problem. Now see it is not about who is having the best AI tool possible. It is about which has the most costefficient AI tool possible in their companies. And how is that achievable? It is achievable by responsibly including a solution that uses this generational ability but smartly in the domain where it is actually required and not just copy pasting every solution in the name of AI revolution or increasing LLM usage. Thank you for watching this video. I hope I'll see you in the next