Understanding Large Language Models: What Powers ChatGPT and Beyond

✅Introduction

Ever asked ChatGPT a question and got a surprisingly helpful response? Or wondered how it seems to “understand” and generate human-like text? Behind the scenes is something incredibly fascinating: a Large Language Model (LLM).

In this blog, we’ll explore in a friendly and interesting way, how Large Language Models (LLMs) like ChatGPT actually work. From the basic building blocks to the fascinating mechanics of transformers, attention and how it all comes to life in real-world applications. By the end, you’ll have a solid grasp of what powers today’s generative AI and a peek into what the future might hold.

🚀 What Is a Large Language Model?

A Large Language Model is an artificial intelligence system trained on huge datasets of human text — books, websites, articles, code, and more. Its core job? To predict the next word.

Yes, at its heart, it’s that simple. But with billions (even trillions!) of parameters and advanced architectures, LLMs go far beyond autocomplete. They can:

Translate languages
Write essays, poems, and code
Answer questions
Summarize documents
Simulate conversation

All by understanding patterns in human language.

🔧 The Core Architecture: Transformers

The backbone of ChatGPT and similar models is a revolutionary architecture called the Transformer, introduced in 2017 by Vaswani et al. in the paper “Attention is All You Need.”

Let’s break it down.

🚨 Self-Attention: The Secret Sauce

Imagine reading a sentence:
“The animal didn’t cross the street because it was too tired.”

What does “it” refer to? The model needs to look at the whole sentence and figure that out.

Self-Attention allows the model to weigh each word’s relevance to every other word, not just in sequence, but in context. This is why transformers are so good at understanding nuance.

🧩 Positional Encoding

Transformers don’t read word-by-word like humans, they process in parallel. So they need a way to understand order. This is handled through positional encoding a technique that adds information about each token's position in the sequence.

🧠 Multi-Head Attention

Instead of using just one perspective, the model looks at the sentence from multiple angles at once. Think of it like having different experts give opinions, and the model combines them to understand meaning better.

🏋️‍♂️ The Training Process: From Pre-training to Fine-Tuning

1. Pre-training: Learning the World

The model is fed a massive amount of text (think internet-scale). Its goal? Predict the next word over and over. This helps it learn:

Grammar
Facts
Reasoning
World knowledge

No human labeling is needed, it’s unsupervised learning at scale.

2. Fine-Tuning: Making It Useful

Once the base model is trained, it’s fine-tuned on curated data for specific tasks (e.g., customer support, medical Q&A). This process adjusts the model to perform reliably on real-world tasks.

3. RLHF: Teaching It Manners

This is where Reinforcement Learning from Human Feedback (RLHF) comes in.

Humans rate answers (like 👍 or 👎), and the model is trained to prefer responses that align with those preferences. This makes the model safer, more helpful and aligned with human values.

🔍 Tokenization: How Text Becomes Numbers

Machines don’t understand words, they understand numbers. So before training, text is tokenized:

“ChatGPT is amazing!” → [“Chat”, “G”, “PT”, “ is”, “ amazing”, “!”]
Each token is mapped to a unique number.

The model operates on these tokens and predicts what comes next.

⚙️ Parameters: The Brain Cells of AI

Models like GPT-4 have hundreds of billions of parameters. You can think of parameters as knobs the model adjusts during training to minimize its prediction error.

More parameters ≠ smarter, but more parameters + better data + clever training = more powerful models.

📈 What Can LLMs Do?

Some popular use cases:

Customer support bots
Content generation (blogs, poems, scripts)
Code generation (like GitHub Copilot)
Data analysis via natural language
Personal assistants (like ChatGPT or Claude)

And that’s just the tip of the iceberg.

⚠️ Limitations of LLMs

Even powerful models like ChatGPT have challenges:

Hallucination – They may generate believable but incorrect info.
Bias – They can reflect biases in the training data.
No real understanding – They mimic intelligence, but don’t “think.”
Context window limits – They forget what’s outside the current context.

🧠 Do They Think?

No, and this is important.

LLMs don’t have consciousness, intentions, or understanding. They’re statistical engines predicting what’s likely to come next. Their fluency can make them seem intelligent, but it’s not the same as human reasoning.

🛠️ Example: Ask a Model Anything

Here’s a simplified Golang example using OpenAI’s API:

(I have created a sample GitHub repo with all the code, mentioned in this blog)
👉 (Check out the repo here)

func GetResponse(client *openai.Client, ctx context.Context, question string) {
    resp, err := client.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
     Model: "gpt-3.5-turbo", // Use "gpt-4" for more advanced responses
     Messages: []openai.ChatCompletionMessage{
      {Role: "user", Content: question},
     },
    })
    if err != nil {
     fmt.Println("Error:", err)
     os.Exit(13)
    }
   // Print the response from ChatGPT
    fmt.Println(resp.Choices[0].Message.Content)
   }

This sends your message to the GPT-3.5 model and prints the response. That’s how services like ChatGPT work under the hood.

Final Thoughts

Large Language Models are one of the most groundbreaking advancements in AI history. From transformers to RLHF, they combine powerful architectures with vast data to generate human-like text.

Understanding them isn’t just for researchers anymore. Whether you're a developer, writer, or curious human, this knowledge gives you insight into the tools shaping our digital future.

Want to Go Deeper?

Read the “Attention Is All You Need” paper.
Explore OpenAI’s API documentation.

📖 For more posts like this, visit VickyBytes.com our go to place for in depth content on AI, Development, DevOps and Cloud-native technologies.

Thank you so much for reading🧡