What Is A Large Language Model (LLM)?

Key Takeaways

A Large Language Model (LLM) is an advanced AI system trained on vast amounts of text data to understand, generate, and process human language.

LLMs are trained using deep learning techniques, particularly neural networks, to predict and generate text based on patterns and context found in large datasets.

LLMs are a key technology behind Natural Language Processing (NLP) applications, enabling tasks like language translation, sentiment analysis, and text generation.

What Is A Large Language Model (LLM)?

A Large Language Model (LLM) is a type of AI that’s really good at understanding and creating human language. It’s trained on huge amounts of text—like books, articles, and websites—so it can learn how language works, from grammar to meaning. Think of it as a super-smart reader and writer that can help with tasks like answering questions, writing stories, or even coding.

Most LLMs use something called the Transformer architecture, which is a way of processing information that helps the model pay attention to the most important parts of a sentence or paragraph. This architecture has two main parts: encoders and decoders.

Types Of Transformers In LLMs

There are three main types of Transformers, each with its own job:

Encoders

👉 What they do: They take in text and turn it into a dense, meaningful representation (like a summary of the text).

👉 Example: BERT (from Google) is an encoder-based model.

👉 Use cases: Great for tasks like classifying text (e.g., is this email spam?) or finding named entities (e.g., spotting names in a sentence).

👉 Size: Usually has millions of parameters (the “settings” the model learns).

Decoders

👉 What they do: They generate new text, one word (or token) at a time, based on what’s already been written.

👉 Example: Llama (from Meta) is a decoder-based model.

👉 Use cases: Perfect for creating text, like chatbots or writing code.

👉 Size: Often has billions of parameters, making them very powerful.

Sequence-To-Sequence (Encoder-Decoder)

👉 What they do: Combine both encoders and decoders. The encoder processes the input, and the decoder generates the output.

👉 Example: T5 or BART are Seq2Seq models.

👉 Use cases: Ideal for tasks like translating languages or summarizing articles.

👉 Size: Typically has millions of parameters.

Most LLMs you hear about—like GPT-4 or Llama—are decoder-based models with billions of parameters!

How Do LLMs Work?

At their core, LLMs are simple: they predict the next word (or token) in a sentence based on what’s come before. A token is a piece of text, like a word or part of a word. For example, the word “interesting” might be split into “interest” and “ing.”

Here’s how it works step by step:

Tokenization: The text is broken into tokens. Instead of using whole words, LLMs use smaller units to be more efficient. For example, “interesting” becomes “interest” + “ing.”

Predicting the Next Token: The LLM looks at the sequence of tokens and guesses what the next token should be. It keeps doing this, one token at a time, until it thinks the sentence is complete.

Special Tokens: LLMs use special markers, like End of Sequence (EOS) tokens, to know when to stop. Different models have different special tokens, like <|endoftext|> for GPT-4 or <|eot_id|> for Llama 3.

Decoding: This is how the LLM turns its predictions into actual text. It can use simple methods (like always picking the most likely next token) or smarter ones (like beam search, which explores multiple possible sentences to find the best one).

Attention: The Secret Sauce

A key part of how LLMs work is something called attention. This helps the model focus on the most important words in a sentence when predicting the next word. For example, in the sentence “The capital of France is…,” the words “capital” and “France” are more important than “the” or “is.” Attention helps the model zero in on what matters.

This is why LLMs can handle long sentences or even paragraphs—they can remember and connect ideas across many words.

Prompting: Guiding The LLM

The way you ask an LLM for something matters a lot. The prompt is the input you give it, and how you word it can change the output. For example:

Bad prompt: “Tell me about dogs.”

Better prompt: “Write a short, fun fact about dogs for kids.”

The better prompt gives clearer instructions, so the LLM knows exactly what you want. Think of it like giving directions to a friend—the clearer you are, the better they can help.

How Are LLMs Trained?

LLMs learn by reading tons of text and trying to predict the next word in a sentence. This is called self-supervised learning because they don’t need labeled data—they just learn from the patterns in the text.

Once they’re trained, they can be fine-tuned for specific tasks, like answering questions or writing code, by training them on smaller, task-specific datasets.

Using LLMs: Local Or Cloud?

You can use LLMs in two main ways:

Run them locally: If you have a powerful computer, you can download and run the model yourself.

Use a cloud API: Services like Hugging Face let you use LLMs without needing fancy hardware. You just send your request over the Internet, and the model does the work for you.

For most people, using a cloud API is easier and doesn’t require a supercomputer at home.

LLMs In AI Agents

Now, here’s where it gets really cool: AI agents use LLMs as their “brain.” An AI agent is like a smart assistant that can do tasks for you, like answering questions, booking appointments, or even writing reports. The LLM helps the agent understand what you’re asking, figure out what to do, and respond in a helpful way.

In short, the LLM is what makes the AI agent smart—it’s the part that understands language, makes decisions, and learns from interactions.

Also Read: A Quick Guide About AI Agents And Their Impact In Crypto