Explain LLMs like I am 5

• Posted 10 May 2025

Whenever I watch a Large Language Model (LLM) produce what seems like intelligent, human-level "thinking," I can't help but wonder: how is this actually working? Let's break it down in the simplest terms possible.

The Magic of Word Connections

LLMs understand words by seeing how they relate to each other. Think of each word as having a special ID card with hundreds of numbers that describe it. These numbers tell the computer how words are connected to each other.

Words as Number Tables

Let's imagine each word has a scorecard that shows how strongly it relates to different concepts:

In this simple example, we can see that "Dog" has a high score for being a pet and being furry, but a low score for being a sound. Meanwhile, "Meow" has low scores for being a pet but high scores for being cat-related and being a sound.

Answering Simple Questions

Question: Do dogs meow?

Answer: Probably not, because:
- "dog" has a high "is a pet" score (0.8) and low "sound-like" score (0.1)
- "meow" has a low "pet-like" score (0.1) and high "sound-like" score (0.9)
- "meow" is closely connected to "cat" (0.9 score for "cat-related")
- The model knows dogs and cats are different animals

I can see how this works for simple facts. But what about more complex questions? For example, if I ask: Who is considered the most innovative prog rock band?

More Complex Knowledge Works the Same Way

It's the same principle! Just with different "dimensions" or characteristics. The LLM knows which bands are associated with prog rock and which ones are considered innovative based on the patterns it learned from reading millions of texts.

What About Those "Billions of Parameters"?

When you hear that a model has 8 billion parameters, it doesn't mean each word has 8 billion numbers describing it. Those parameters are part of the LLM's internal machinery, not just word descriptions.

Think of an LLM as a super-smart robot chef who knows how to make thousands of recipes. The parameters are like the robot's recipe book—a huge set of instructions that tell it how to mix ingredients (words, ideas, and patterns) to create answers.

For an 8-billion-parameter model, those numbers are spread across the LLM's "layers," which are like pages in the recipe book. Each layer helps the robot process words in different ways—understanding grammar in one layer, context in another, and creativity in yet another.

The Toy Box Analogy

The vector database is like a toy box that stores representations of words (vectors for "dog," "cat," "King Crimson," etc.) so the LLM can look them up quickly. The database doesn't have 8 billion attributes per word. Instead, it stores vectors with a fixed number of dimensions (maybe 300 numbers per word), and there could be millions of vectors (one for each word, phrase, or concept).

Think of it like a toy box where each toy has a card with 300 stickers, but each sticker's color is an RGB hex value. You don't need millions of stickers because those 300 stickers capture nuance through their precise values—like how #AB274F and #F19CBB are different shades of pink. In the real system, each dimension is a floating-point number with many decimal places of precision.

How Does the LLM Learn These Values?

During training, the LLM examines billions of sentences and learns patterns, like "dog" appearing with words like "pet," "bark," and "furry," or "King Crimson" appearing with "prog rock" and "innovative." It compresses these patterns into a fixed number of dimensions (like 300) using sophisticated mathematics.

Each dimension doesn't represent just one specific feature but a blend of features. For example, dimension #42 might capture a mix of "pet-like + animal-like + a bit of loyalty," while dimension #137 captures "sound-like + noise + a bit of emotion."

Through many iterations, the model refines these values—adjusting from 0.352423211 to 0.352422009 as it learns more precisely how words relate to each other.

Why Can't It Get It Right the First Time?

When an LLM begins training, it's like a brand-new toy robot with an empty brain. Its parameters start as random numbers, meaning they don't yet know how to describe words correctly.

When it first tries to represent "meow," it might use these random parameters and get something wildly wrong—like thinking "meow" is strongly associated with "pet" (0.7) and weakly with "sound" (0.3), instead of the correct relationship.

It's like the robot chef picking random ingredients—sugar, salt, and ketchup—to bake a cake. The result will taste weird until it learns the right recipe!

The training data (sentences like "Meow is a sound") acts as the robot's teacher. But at the start, it hasn't "read" enough examples or figured out the patterns. Each time it makes a mistake, it adjusts its recipe slightly—like tasting a bad cake and saying, "Too much salt! Let's use less next time."

How Word2Vec Works: The Guessing Game

One popular way to learn these word relationships is through something called Word2Vec, which works like a fun guessing game:

1. The Guessing Game: When the model sees the word "meow," it tries to guess what words might be nearby—like "cat," "sound," or "pets."
2. Special Number Cards: Every word gets special number cards (these are the "vectors"). At first, these cards have random numbers.
3. How to Play: When the model sees "meow" in "Meow is a sound cats make," it uses "meow's" number cards to guess the words around it.
4. Getting Better: At first, its guesses are terrible! Using "meow's" random numbers, it might guess "dog" instead of "sound."
5. Learning from Mistakes: The game tells the model how wrong it was. If it guessed "dog" but the correct answer was "sound," it gets a big "wrongness score."
6. Fixing the Cards: The model adjusts the numbers on "meow's" cards to make better guesses next time. After many tries with lots of sentences, "meow's" number cards get really good at predicting words like "cat" and "sound."

After millions or billions of examples, the model develops a rich, nuanced understanding of how words relate to each other. That's how LLMs can seem so smart—they've seen patterns in language that help them make educated guesses about what words should come next in any context.

So next time you're amazed by an LLM's response, remember: it's not magic or real intelligence—it's just a very sophisticated pattern-matching system that's learned the statistical relationships between words from reading more text than any human could in a lifetime.

*How the LLM Gets Feedback

Small Batches: Instead of looking at all training data at once, the model looks at small batches (maybe a few hundred sentences) at a time. Quick Check: For each batch, it:

• Makes predictions using current vector
• Compares predictions to what actually appears in those sentences
• Calculates how wrong it was (the loss score)

The math of the loss function automatically tells which direction to adjust numbers. It's like a special compass that points toward "better" without checking the whole map. The model makes tiny changes to its vectors based on this feedback. These small steps are called "gradient updates." Then it grabs the next batch of examples and repeats. Eventually, it will have seen all training data, which completes one "epoch." Training usually involves multiple epochs.

It's like learning to cook by tasting small bites as you go. You don't need to eat the whole pot to know if you need more salt - just a small taste gives you feedback! The clever part is that the loss function is designed to automatically produce a mathematical "direction" that tells exactly how to adjust each number to improve, without needing to try every possible combination or look at all data at once.

*Real time queries: Attention

“The cat slept on the mat.” Each toy’s card gets three stickers: a Question Sticker, a Key Sticker, and an Info Sticker. The Question Sticker says, “What am I looking for?” (for "slept," it might ask, “Who’s sleeping?”). The Key Sticker says, “Here’s what I’m about!” (the "cat" toy’s key says, “I’m a pet!”). The Info Sticker holds the toy’s actual story details (the "cat" toy’s info says, “I’m furry and sleep a lot!”).

The spotlight looks at the Question Sticker from the toy you’re working on (like "slept") and compares it to the Key Sticker of every other toy in the box. If the stickers match well (like "slept" asking “Who’s sleeping?” and "cat" saying “I’m a pet!”), that toy gets a bright glow. If they don’t match (like "slept" and "which"), the toy gets a dim glow. This matching happens super fast because the spotlight checks all toys at once, like shining light across the whole toy box in one go!

When you ask, “Do dogs meow?” the spotlight springs into action in real time! It grabs the toy cards for “dog,” “meow,” and other words in your question. These cards already have their numbers set from training (like “meow” being 0.9 cat-related). It compares the Question Sticker (“What’s meow about?”) with the Key Sticker of every toy (“I’m a cat sound!” for “meow”). It gives each toy a glow score (like “meow” gets a bright glow, “dog” gets a dim glow). It mixes the Info Stickers from the brightest toys to answer, “Nope, dogs don’t meow—cats do!”

Why It’s Fast During a Query: The toy box doesn’t change its recipe book or toy cards when you ask a question. It just uses what it learned during training. The spotlight’s parallel magic means it looks at all toys at the same time, like shining a giant flashlight across the whole toy box in a snap!