How Do LLMs Work?
So you’ve probably chatted with an AI before, (If not STOP Reading this blog and go try an LLM!!!!)— maybe it answered your question, helped with homework, or even made you laugh. That AI was likely powered by an LLM, or Large Language Model.
But how does it actually work? Today, we’re going to peek under the hood and see the amazing “how” – how do these LLMs actually work their word magic?
Let’s break it down — in simple language — and find out how LLMs really work, from start to finish.
What’s the Goal of an LLM?
An LLM’s main job is very simple:
It tries to guess what comes next in a sentence.
That’s it. But this simple skill — predicting the next word — is powerful. It can:
Answer questions
Tell stories
Write essays
Solve math problems
Translate languages
All by predicting one word at a time! Let’s see how this goal can be achieved
Ingredient 1: Training Data — The Learning Phase
Imagine you want to teach a robot to be amazing at understanding and writing English. You can’t just give it a dictionary and a grammar book and say, “Okay, learn!” Humans don’t learn language that way, and neither do LLMs.
Instead, LLMs learn by looking at tons and tons (and TONS!) of examples. It’s a bit like how you learned to talk – by listening to people around you, trying out words, and slowly figuring out what makes sense.
Before an LLM can make smart guesses, it has to learn.
Before an LLM can do anything, it needs to “read” an unbelievable amount of text. This is called its training data.
What does it read?
It’s trained on:
- Books: Millions of books – storybooks, science books, history books, joke books, everything!
- Websites: Huge chunks of the internet – articles, blogs, news sites, discussions (the safe and helpful parts, of course!).
- Conversations: Scripts from movies, plays, and even some (anonymous and privacy-protected) online chats.
- Code: Instructions to create software applications
And Millions and millions of pages of it. Remember the “L” for Large!
This massive collection of text is like the LLM’s school, library, and playground all rolled into one. It’s where it sees how words are used in every possible way. The more good quality text it sees, the better it gets at understanding language.
Ingredient 2: Turning Words into Secret Codes (Embeddings)
Now, here’s something super cool. Computers don’t really understand “words” like “cat” or “happy” the way we do. Computers love numbers!
So, the first clever trick an LLM does is to turn every single word into a special secret code made of numbers. This is called an “embedding.”
Let’s zoom in on one of the coolest tricks LLMs use: Embeddings. This sounds super technical, but I promise, we can break it down with some fun examples!
Remember how I said computers love numbers, not words? Well, embeddings are the magical way LLMs turn words into special number codes.
The Secret Number Codes for Words: What are Embeddings?
An “embedding” is basically a list of numbers that represents a word.
It’s like giving each word its own unique secret code or its own set of GPS coordinates on a giant “meaning map.”
Let’s think about it like this:
Example 1: The Animal Neighborhood Map
Imagine a huge, invisible map. We’re going to place animal words on this map.
The word “dog” gets a spot.
The word “puppy” would get a spot VERY close to “dog” because they are super similar in meaning.
The word “cat” would also be nearby, because it’s another common pet, but maybe not as close as “puppy” is to “dog.”
Words like “bark,” “leash,” and “fetch” would also be in the “dog” neighborhood because they are strongly related to dogs.
A word like “kitten” would be very close to “cat.”
Now, think about the word “car.” Where would that go on our animal map? Probably very, very far away from “dog” and “cat,” right? Because a car has nothing to do with animals.
What about “banana”? Again, far away from the animal neighborhood.
How is this a “list of numbers”?
The “spot” or “address” of each word on this meaning-map is actually defined by a list of numbers. It might not be just two numbers like on a regular map (X and Y coordinates). For LLMs, it’s often a list of hundreds of numbers for each word!
So, the embedding for “dog” might look something like this (these are just made-up numbers to show the idea):
[0.2, -0.5, 1.3, 0.8, -2.1, … and hundreds more numbers …]
And the embedding for “puppy” might be:
[0.3, -0.4, 1.2, 0.9, -2.0, … and hundreds more numbers …]
Do you see how the numbers are similar, but not exactly the same? That similarity in their number codes tells the LLM that “dog” and “puppy” are closely related in meaning.
The embedding for “car” would have a list of numbers that are very different:
[5.7, 2.0, -3.4, 0.1, 6.8, … and hundreds more numbers …]
How does the LLM get these number codes?
During that massive training process where the LLM reads billions of sentences, it learns to create these embeddings. It figures out that words appearing in similar contexts (like “I walked my ___” often being “dog” or “puppy”) should have similar number codes. It’s like the LLM is building this giant meaning-map as it reads.
Example 2: The “Royalty” Club
Let’s think about words related to kings and queens.
“King” gets its number code (embedding).
“Queen” gets its number code. These will be somewhat similar because they are both rulers.
“Prince” and “Princess” will have number codes close to “King” and “Queen” respectively, and also to each other.
Words like “crown,” “throne,” and “palace” will also have number codes that place them in this “royalty” neighborhood on the meaning-map.
A word like “bicycle” will have a number code that places it very far away.
Why is this so powerful? Word Math!
This is where it gets mind-blowingly cool. Because words are now represented by lists of numbers, the LLM can do a kind of “math” with them!
The most famous example is:
Take the number code for “King”.
Subtract the number code for “Man”.
Add the number code for “Woman”.
The resulting list of numbers will be VERY, VERY close to the number code for “Queen”!
It’s like saying: King - Man + Woman ≈ Queen
This shows that the LLM, through these number codes, has learned the relationship: “A King is to a Man what a Queen is to a Woman.”
It’s not just memorizing facts; it’s capturing the relationships between concepts in these numbers.
Another example could be:
Paris - France + Germany ≈ Berlin
(The capital city Paris, take away its country France, add another country Germany, and you get something very close to Germany’s capital, Berlin).
So, what do embeddings help an LLM do?
Understand Meaning: They help the LLM grasp that “happy” and “joyful” are similar, even if the letters are different.
Understand Relationships: As we saw with “King - Man + Woman = Queen,” it can understand how concepts relate.
Find Similar Words: If you ask it for a synonym, it can look for words whose number codes (embeddings) are close to the original word’s code.
Better Predictions: When predicting the next word, if it sees “The fluffy…”, and “fluffy” is often near “cat” or “dog” on its meaning-map, it’s more likely to predict one of those words.
Handle New Words (Sort Of): Sometimes, even if it hasn’t seen an exact word, it can guess its meaning if it’s made up of parts it does know (like “un-fluffy-ness”).
Embeddings allows an LLM to move beyond just seeing words as strings of letters and start to “understand” (in its own computer way) what they mean and how they connect to each other. It’s a foundational piece of the puzzle for how LLMs work their amazing word magic!
Ingredient #3: The Super-Smart Learning Machine (Neural Networks)
Okay, so the LLM has read everything and turned words into number codes. Now what? How does it actually learn? This is where something called a Neural Network comes in.
Don’t let the fancy name scare you! Think of a Neural Network like a giant team of super-tiny, super-fast workers, all organized in layers.
These workers are grouped into a model called a Transformer.
What’s a Transformer?
The Transformer is the super-smart architecture that LLMs use. Think of it like a huge, brain-shaped LEGO set where each block has a job:
Some blocks read words
Some blocks pay attention to important parts
Some blocks guess the next word
Together, these blocks help the LLM “understand” what you’re saying — and what to say back.
All those workers are organized in three sections in the transformer
- Input Layer: This is where the word codes (embeddings) go in.
- Hidden Layers: This is where the real magic happens! There can be many of these layers. Each layer of tiny workers looks at the information from the layer before it, does some calculations, and passes on what it “thinks” to the next layer. Each layer might look for different kinds of patterns – one layer might be good at spotting grammar, another at noticing if the topic is about animals, another at figuring out if the tone is happy or sad.
- Output Layer: After going through all the hidden layers, this layer gives the final result – like predicting the next word in a sentence.
How do these “workers” learn? It’s all about predicting the next word. This is the main game LLMs play during training.
Imagine the LLM is given this sentence from its training data: “The fluffy cat sat on the ____.” The LLM’s job is to guess the missing word.
- Maybe its first guess is “banana.” The training system says, “Nope, that’s not very likely!”
- The LLM then looks at the correct answer from the training data, which might be “mat.”
- Now, here’s the clever bit: The LLM adjusts all its tiny “workers” and their connections (these connections are called parameters or weights – imagine them like tiny volume knobs) just a little bit, so that next time it sees a similar sentence, it’s more likely to guess “mat” (or something sensible like “chair” or “rug”) and less likely to guess “banana.”
It does this billions and billions of times, with billions of different sentences! Each time it makes a mistake, it learns. Each time it gets it right, it reinforces what it learned. All those tiny adjustments to its billions of “knobs” (parameters) slowly make the LLM incredibly good at understanding how words fit together and what words are likely to follow other words.
These parameters are what makes an LLM “Large.” A big LLM might have hundreds of billions, or even trillions, of these adjustable knobs! That’s a lot of learning!
Ingredient 4: Attention – The Secret Sauce
Transformers use something magical called attention.
Let’s say you ask:
“Why did the chicken cross the road?”
The LLM pays attention to words like “chicken” and “cross” and “road.” It learns that those words are important.
It doesn’t just look at the last word — it looks at the whole sentence to figure out what matters most.
This makes it way smarter than older models that just looked at one word at a time.
A Real-Life Example: “Smart Highlighter”
Think of attention like a smart highlighter. The model reads a sentence, and it automatically highlights the important words depending on what it’s trying to do.
Let’s say the sentence is:
“Sara gave her dog a bath because it was dirty.”
If the model is trying to figure out what “it” refers to, the attention mechanism will highlight the word dog, because that’s the most likely thing that was dirty—not Sara!
How Does It Work Inside?
Each word is turned into a number (embeddings).
The model does some math to figure out how related each word is to the others.
This math gives each word a score—kind of like a “how important is this word right now?” score.
The model uses those scores to focus more on important words.
This is called “self-attention”, because the model is paying attention to itself—every word in the sentence is looking at every other word.
Why Is It So Cool?
Attention is one of the superpowers that make transformers so good at:
Understanding long sentences 🧾
Translating languages 🌍
Writing poems and stories ✍️
Answering questions like a smart friend 🎓
Without attention, a model would just read words one by one and miss the big picture.
Putting It All Together: How an LLM “Talks” or “Writes”
So, after all that training, how does an LLM actually generate a story, answer your question, or write a poem?
It basically does it one word at a time, using everything it has learned.
- You give it a prompt: For example, “Write a story about a friendly dragon…”
- It processes your prompt: It turns your words into number codes (embeddings) and runs them through its neural network.
- It predicts the most likely next word: Based on your prompt and all the patterns it learned during training, it calculates the probability (the chance) of every possible word in its vocabulary being the next word. It then picks one of the most likely words.
- So, after “Write a story about a friendly dragon…”, it might predict the word “who.”
- It adds that word to the sequence: Now it has “Write a story about a friendly dragon who…”
- It predicts the next next word: It takes this new, slightly longer sequence and again predicts the most likely word to come after “who.” Maybe it predicts “lived.”
- And so on…: It keeps doing this, adding one word at a time – “Write a story about a friendly dragon who lived in a…”, then maybe “sparkling,” then “cave,” then “filled,” etc. – until it thinks the sentence or paragraph is complete, or it reaches a certain length.
It’s like building a tower with LEGO bricks, one brick at a time. For each new brick, it looks at all the bricks it has already placed and chooses the next brick that it thinks will fit best and make the coolest tower, based on all the LEGO towers it’s “seen” before.
Sometimes, to make the text more interesting and less predictable, it might not always pick the absolute most probable word, but one of the top few most probable words. This adds a bit of creativity and randomness, so it doesn’t say the exact same thing every time.
Final Thoughts
Large Language Models (LLMs) are like super-smart word calculators. They read tons of text, learn patterns, and use a special brain called a Transformer to guess what comes next in a sentence.
They’re not perfect — they can make mistakes, get confused, or be biased — but they’re powerful tools that help us write, learn, and create.
Just remember:
An LLM doesn’t understand like a human.
But it’s really good at pretending it does.
Use it wisely — like a helpful robot assistant for words!