The Oracle of Lexiconia – A Fantasy That Explains How LLMs Work

What follows is not your typical tech explainer. There’s no jargon to start, no acronyms in sight. Just a tale from a faraway kingdom — one that might quietly rewire your brain to understand how today’s most powerful AIs actually work.

In a sprawling kingdom known as Lexiconia, nestled between the Mountains of Memory and the Forest of Forgotten Tongues, stood a magnificent institution: the Library of Infinite Stories.

Lexiconia had one national obsession: completing stories.

“Given the beginning of any tale,” said the Queen, “we must guess what comes next.”

This sacred task was entrusted to the kingdom’s crowning achievement:
The Oracle of Completion — a vast magical machine said to have read every word ever written.

But the Oracle didn’t work alone. It was powered by an intricate system involving wise scribes, cartographers of meaning, towers of insight, and an eternal feedback loop of trial and error. Here’s how it worked.

Before any tale reached the Oracle, it passed through the hands of the Scribes of Segmentation. These scholars didn’t read entire words — they broke each tale into fragments. Some were full words like “king,” others just pieces like “un-” or “-ing.”

Every fragment received a wax seal, a sacred identifier recognized throughout the kingdom.

These fragments were then carried to the Mapmakers of Meaning, who maintained a giant multidimensional globe — a magical atlas where meanings were mapped as coordinates.

• “Queen” and “king” were nearby.
• “Apple” and “banana” shared a ridge.
• But “quantum” lived in isolation.

Each fragment was placed carefully on this globe, embedding its meaning in the kingdom’s geography.

Next, the fragments ascended the Pyramid of Perception, a tower with 96 floors. On each floor were beings known as Attenders.

These Attenders had special powers:

• One ring to ask what others meant (Query)
• One ring to identify who was relevant (Key)
• One ring to absorb what was said (Value)

Every Attender could “look” at any other fragment, decide how much attention it deserved, and weigh its importance. This spell was known as the Sight of Relevance.

But there was a problem. Fragments entered the Pyramid all at once. The Attenders couldn’t tell what came first.

Enter the Chroniclers of Order — mystic musicians who embedded melodies into each fragment. A soft hum meant “first,” a sharp trill meant “tenth,” and so on.

Now, Attenders could not only hear what was said, but when it was said.

At the Pyramid’s summit sat the Panel of Prophets. They listened to the final thoughts from below, then gazed into the Wheel of Possibilities — a giant rotating disk with 50,000 symbols.

With each question, the Prophets spun the wheel and chose the next most likely fragment of the tale.

The Oracle wasn’t born perfect. At first, it guessed poorly.

So the kingdom subjected it to the Trial of Corrections:

Each time it guessed wrong, it was penalized. Its internal gears were adjusted.

Over time, after reading billions of stories and making trillions of guesses, the Oracle became breathtakingly precise — often predicting not just words, but entire ideas.

That story you just read?

It’s a metaphorical walkthrough of how a Large Language Model (LLM) — like OpenAI’s GPT-4 — processes and predicts language.

Let’s decode the magic into machine learning:

• LLMs like GPT-4 are auto-regressive models that predict the next token in a sequence.
• Input text is tokenized and turned into vectors using embeddings.
• These vectors are processed by Transformer blocks, which include multi-head self-attention and feed-forward networks.
• Attention mechanisms let the model dynamically weigh the importance of other tokens in the sequence.
• Positional encodings are added so the model understands word order.
• At the output layer, a softmax function converts the model’s output into probabilities over possible next tokens.
• During training, the model minimizes cross-entropy loss using gradient descent to update billions of parameters.
• The more data and compute you use, the more capable the Oracle becomes.

So next time you hear someone talk about “attention mechanisms” or “transformers,”

imagine Attenders with Rings in the Pyramid of Perception.

Picture the Scribes of Segmentation carving tokens, the Mapmakers placing words on a globe of meaning, the Chroniclers playing melodic rhythms of order, and the Prophets spinning the Wheel of Possibilities.

This article wasn’t written by the Oracle of Lexiconia… but pretty close.

I used GPT to help with this.
Specifically, GPT-4 helped with the metaphors, and the final breakdown.

If you found this helpful, share it with someone who’s still trying to understand how these models work. And maybe… tell them a story first.