Decoding the Complex World of LLMs

Ever feel lost in the AI race, confused by jargon like tokens, temperature, and vectors? This blog will break down all the confusing LLM jargon into simple, human terms.

What are LLMs?

Imagine a super-smart human who has read every book, article, and website in the world. This smart human can answer almost any question you ask that’s kind of what a Large Language Model (LLM) is.

LLMs are like massive computer brains trained on huge datasets of text, images, and videos. They learn from examples to understand and generate text that sounds like a real human wrote it.

These models learn using a method called deep learning, which is like teaching a computer by giving it tons of examples. They use neural networks (inspired by how our brains work) to understand relationships between words and phrases.

At their core, LLMs are next-word predictors. They’re trained to predict the most likely continuation of text based on the context they’ve been given.

user query transformation / next word prediction

Transformer

A Transformer is like a very fast reader and writer.

It looks at words in a sentence to see what they mean and then uses that understanding to create new sentences.

One key idea Transformers use is called self-attention (we’ll get to that soon). Because Transformers can look at many parts of text at the same time, they’re faster than older models that processed one word at a time.

Transformers can learn on their own without a teacher this is known as self-learning. It’s like figuring out how to ride a bike by trying over and over. Along the way, they learn grammar, languages, and useful facts by finding patterns in the data they read.

Often, this self-learning is combined with other methods to make results more accurate and controlled.

Encoder and Decoder

Think of the encoder and decoder as the reading and writing team of LLMs.

• The Encoder is the reader. It takes your input like “how loops work in Python” and turns it into special code that the LLM understands. This is like translating English sentences into tokens (numbers) that a computer can process. The encoder uses attention mechanisms to focus on the most important parts of the input.

• The Decoder is the writer. It takes that code and turns it back into English this time as an answer. It uses its understanding of language and the encoded context to produce a meaningful response.

Tokenization

Humans communicate in languages like Japanese, Hindi, or English but AI models don’t “understand” languages directly. They understand numbers, specifically structures called tokens.

Tokens are the building blocks of any LLM’s input and output. They can be individual words, parts of words, or even characters depending on the model.

generated token with respected words ( model used: deepseek-r1)

Tokenization is the process that converts human language into these numerical tokens that AI models can understand. It’s what bridges the gap between human text and machine learning.

Every AI model whether it’s Gemini, GPT-4o, or DeepSeek uses its own tokenizer to interpret and generate human-like text.

tokenization

Try it here: https://tiktokenizer.vercel.app

Vectors

Vectors are numerical representations of words and tokens.

They allow models to perform mathematical operations on text like measuring how similar two words are. When words are converted to vectors, models can calculate relationships between them and understand meaning.

vectors

The quality and dimensionality of these vectors determine how well a model understands and generates language.

Each model represents words differently even for the same word.

Embeddings

Embeddings are a special kind of vector representation that captures semantic meaning. Unlike simple encodings, embeddings place similar words close together in a high-dimensional space.

For example:

• “King” and “Queen” have embeddings that are close together.
• “King” and “Automobile” are far apart.

Embeddings usually have hundreds of dimensions, and each dimension represents some aspect of meaning. These representations are learned during pre-training by analyzing patterns of word co-occurrence across massive text corpora. They are the foundation of how LLMs understand language they convert text into mathematical form while preserving meaning.

Softmax

When an LLM generates text, it needs to decide which word to use next. There are many possible options so how does it choose?

softmax

This mechanism helps LLMs produce text that’s coherent, grammatically correct, and contextually relevant.

Temperature

Imagine you’re writing a story and want it to be more creative and surprising.

That’s what temperature controls in an LLM.

• High temperature: “Surprise me!” → More random, creative, and diverse responses.
• Low temperature: “Keep it simple.” → More focused, predictable, and conservative responses.

Technically, temperature modifies how probabilities from the softmax are distributed:

• Higher temperatures spread probabilities evenly, encouraging variety.
• Lower temperatures sharpen them, making the model stick to the most likely choices.

Set it too high, and the output gets chaotic like ordering an ice cream flavor that doesn’t exist.

Self-Attention

person reading a newspaper

Picture this: you’re reading a newspaper. You don’t focus on every single word your eyes jump to key phrases and names. That’s exactly how self-attention works.

The model assigns different importance levels to each word in a sentence, focusing on what matters most. This helps it understand context, relationships, and meaning between words.

Without self-attention, LLMs wouldn’t be able to generate responses that feel natural or contextually accurate.

It’s computationally expensive, but it’s the core reason why modern models like GPTs are so powerful.

Knowledge Cutoff

LLMs are trained on massive datasets but only up to a certain point in time. This is called the knowledge cutoff.

After that date, the model doesn’t know about new events it’s like asking someone who hasn’t read the news in months.

Developers update models occasionally, which changes this cutoff date.

Vocabulary Size

When tokenizing text, the model builds a list of all tokens it has seen during training this is its vocabulary.

The vocab size is the number of unique tokens it knows.
A larger vocabulary lets a model understand more words and phrases.

If it encounters a token it doesn’t recognize, it uses clever methods to break it down and still make sense of it.

Conclusion

That wraps it up!

We’ve simplified the big words Transformers, Embeddings, Self-Attention, Softmax, and more into digestible concepts.

You now understand the key parts that let LLMs read, write, and think like humans.