What is a Large Language Model (LLM)?

AI is doing some incredible things these days—even helping me write this post! Over the past six months, I've been diving deep into AI, using it in my work and daily life. As an engineer, I couldn't resist the urge to understand how tools like ChatGPT and Claude work.

It's been an incredible learning journey, and over the next few days, I'll be breaking down everything I've learned—from insights to challenges—and how it all led me to build my own AI Agent.

That's right—I learn best by building! 🚀 And at the end of this series, I'll be sharing the AI Agent I built with you all. Stay tuned!

You can find more in-depth resources to understand LLMs, transformers, and AI technology at the end of this article.

What are Large Language Models (LLMs)?

To start, let's talk about Large Language Models (LLMs) and why AI is suddenly everywhere. LLMs are advanced AI systems trained on massive datasets containing billions or even trillions of words from books, articles, websites, and other text sources. These models learn statistical patterns in human language to understand context, generate coherent responses, and process complex linguistic tasks.

What makes them "large" isn't just the data—it's also their architecture. Modern LLMs like GPT-4, Claude 3.5, and Gemini contain hundreds of billions of parameters (think of these as the "neurons" in an artificial brain). These parameters are fine-tuned during training to capture everything from grammar rules to world knowledge, cultural nuances, and even reasoning patterns.

They're part of what we call Generative AI because they don't just classify or analyze text—they create new content. Under the hood, they use deep learning architectures called transformers (like GPT—Generative Pre-trained Transformer), which we'll dive into later.

Think of LLMs as a Super Smart Friend

Imagine you have a super smart friend who has read millions of books, articles, and websites. This friend doesn't just memorize everything but can also understand patterns in language, answer questions, and even write stories or code.

That's what a LLM is! It's a type of AI trained on massive amounts of text so it can predict what words should come next in a sentence. This is how it can chat with you, help with homework, write poems, or even explain complex topics in simple terms!

How LLMs Work: Tokens and Prediction

Here's where it gets fascinating—LLMs don't actually "see" words the way we do. Instead, they break down text into smaller units called tokens. A token might be a whole word, part of a word, or even punctuation. For example, the word "understanding" might be split into "under", "stand", and "ing" tokens.

The magic happens through next-token prediction. Given a sequence of tokens, the model calculates probabilities for what should come next. It's like having an incredibly sophisticated autocomplete that considers not just the immediate context, but the entire conversation history, writing style, and even implied meaning.

For instance, if you type "The capital of France is", the model assigns high probability to "Paris" and very low probability to "banana". But it's not just memorizing facts—it's understanding patterns, context, and relationships.

While predicting the next word isn't new (think of your phone's keyboard suggestions), today's LLMs are remarkably powerful because they:

• Consider much longer context (thousands of tokens vs. just a few words)
• Understand semantic relationships and abstract concepts
• Can maintain coherent conversations across multiple topics
• Generate creative and contextually appropriate responses

You can experiment with tokenization yourself using OpenAI's tokenizer tool to see how different models break down text.

The Power of Transformer Models

Transformer models revolutionized AI by introducing a game-changing "attention mechanism" that fundamentally changed how machines understand language. Here's what makes them special:

Self-Attention: The Secret Sauce

Unlike older models that processed text word by word (like reading a sentence from left to right), transformers use "self-attention" to look at all words in a sentence simultaneously. Each word can "attend" to every other word, figuring out which ones are most important for understanding the current context.

Imagine you're reading the sentence: "The bank can guarantee deposits will eventually cover future tuition costs." The word "bank" could mean a financial institution or the side of a river. The attention mechanism helps the model focus on words like "deposits," "guarantee," and "tuition" to understand we're talking about a financial bank, not a riverbank.

Parallel Processing Power

This attention mechanism also makes transformers incredibly efficient to train. While older models had to process text sequentially (waiting for each word before moving to the next), transformers can process entire sequences in parallel. This is why we can train models on massive datasets in reasonable timeframes.

Multi-Head Attention

Transformers actually use multiple "attention heads" simultaneously—think of it as having several different perspectives analyzing the same text. One head might focus on grammatical relationships, another on semantic meaning, and yet another on long-range dependencies. This multi-faceted analysis is what gives modern AI its nuanced understanding.

This architecture is why ChatGPT can maintain context across long conversations, why Claude can analyze complex documents, and why these models seem to "understand" rather than just pattern-match.

What Can Modern LLMs Actually Do?

The capabilities of today's LLMs are pretty mind-blowing, but it's important to understand both their strengths and limitations:

Current Capabilities

• Multimodal Understanding: Models like GPT-4V and Claude 3.5 can analyze images, charts, and documents alongside text
• Code Generation: They can write, debug, and explain code in dozens of programming languages
• Complex Reasoning: Solving multi-step problems, mathematical equations, and logical puzzles
• Creative Tasks: Writing stories, poems, scripts, and even generating ideas for creative projects
• Language Translation: Near-human quality translation between hundreds of languages
• Document Analysis: Summarizing long documents, extracting key information, and answering questions about content

Important Limitations

• Knowledge Cutoff: They only know information up to their training date (though some now have web access)
• Hallucinations: Sometimes generate confident-sounding but incorrect information
• No Real Understanding: They excel at pattern matching but don't truly "understand" like humans do
• Context Limits: Even with large context windows, they can lose track of information in very long conversations
• Inconsistency: May give different answers to the same question asked in different ways

The Bottom Line

LLMs are incredibly powerful tools that can augment human capabilities in amazing ways. They're best used as intelligent assistants rather than authoritative sources of truth. Always verify important information, especially for critical decisions!

Learning Resources

One blog post can't cover everything about LLMs, so let me share what helped me understand them better. Out of all the resources I explored, a few were particularly insightful. You can find them here: 🎁 Learn AI. I'll continue updating this page whenever I discover more valuable learning resources and as I write more about my journey of building an AI agent.

If you've ever wanted to dive into AI but felt overwhelmed, follow along—I'll break it down over the next few days!