u/DevelopmentNo7939

I wanted to research how to understand AI better. But suddenly, I found that before LLMs, I learned that in the market, there are different categories of LLMs.

Some LLMs are instant, like within seconds, they reply. And some LLMs, they take time to give the answer.

So, if I talk about the first category, what I learned about was

speed models, meaning imagine, like you gave a prompt, and you got your answer immediately without wasting any time. So, these are the speed models. Speed tells you that it gives you a speedy, immediate answer. For example, GPT4o mini or Gemini Flash.

Then we have reasoning models. So, reasoning models give you a slightly slow answer, but they try to give an accurate answer. So, reasoning models are those that take time to process. For example, Claude Opus.

Then we have hybrid models. This hybrid model is the owner of its company, which means it will give you an answer quickly, but when it feels like it, it processes for a long time, and when it feels like it, it answers within seconds. So, we call it a hybrid model. For example, Gemini 1.5 and Claude 3.5.

Then we have SLMs, Small Language Models. So, these are capable enough that on your laptop and phone, they can live and work without any internet, without any cost. These are very pocket-friendly.

So, its examples are Mistral and Gemma.

What changed my perspective is realizing that bigger models equal better..

I was wrong. It depends completely on which category of model it is.

So, curious which category of model you all are most interested in or currently using.

So, I decided to move one step back and realized that before understanding LLMs, I first needed to understand the chain behind them:

AI → Machine Learning → Deep Learning → LLMs

It all starts with Artificial Intelligence (AI). The goal of AI is to make machines intelligent enough to perform tasks that normally require human intelligence. AI is a huge field it's not just ChatGPT. Siri, Alexa, Face Unlock, Netflix recommendations, and many other technologies are all examples of AI.

Then comes Machine Learning (ML). Earlier, programmers had to write every single rule to make a machine "smart." But with ML, instead of writing endless rules, we feed machines large amounts of data, and they learn patterns on their own. It was a huge step forward.

Next is Deep Learning (DL), which is a more advanced form of Machine Learning. It uses neural networks inspired by the human brain, making it possible to solve much more complex tasks like image generation, speech recognition, and even self-driving cars.

Finally, we reach Large Language Models (LLMs). These models are trained on massive amounts of text from books, articles, and documents to learn language patterns. That's why they can answer questions, write stories, generate code, summarize information, and have human-like conversations. Some LLMs can also access real-time information when connected to external tools.

The biggest takeaway for me is that everything has a process, and LLMs are no exception. Understanding the foundation makes everything else much easier.

Still a long way to go, but one step closer every day.