Inside the World of LLMs: Technology, Challenges, and Future

Introduction

In recent years, we have all become aware of the “AI revolution” and how AI is now part of our daily lives. Just like we used to search things on Google 1-2 years ago, today we use AI tools like ChatGPT and Gemini. But have you ever wondered how these AI models actually work and what they do behind the scenes?

What is AI and LLM’s

Nowadays, using AI for our daily tasks is common, but have you ever thought about what AI really is? In simple terms, we can say that “any machine that can mimic human intelligence is known as AI (Artificial Intelligence).” This simply means that a machine is smart enough to give answers and think like a human.

But if we talk specifically about LLMs (Large Language Models), which are just another type of AI (the most common LLMs are ChatGPT, Claude, etc.), we can define them as “AI systems that use deep learning to process, understand, and generate human language.” This means that LLMs analyze a query, process it based on their training, and produce an output in human language.

To train LLMs, a massive amount of data is required. Using neural network architectures called transformers, LLMs are trained to recognize patterns and relationships between words and phrases, and to structure language in order to generate a suitable answer based on the query.

Rise of LLM’s

LLMs got into the mainstream after the release of GPT-3 by OpenAI in 2020, which had 175 billion parameters and was an instant hit due to the precision and accurate answers given by GPT-3 compared to other LLM models at that time, revolutionizing natural language understanding worldwide.

Future of LLM’s

If we follow the current trend of LLMs and AI, it seems very promising. Even though we are not yet using their full potential, they have the power to lift the world one step higher. As a part of this changing world, I can say it will be exciting to see what happens in the next five years and how big companies compete for dominance.