Decoding AI Jargon with Chai

💡 Imagine: You are teaching a smart robot to write movie reviews.

You want this robot to read reviews online and write new ones that make sense, sound human and are relevant. Let’s walk through the terms with this single story:

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🧠 Transformers — The Robot’s Brain

The robot uses a special brain called a Transformer.
This brain is great at understanding and generating text. Whenever you give it a sentence, it can predict what comes next or write a new sentence.

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🔍 Encoders — The Understanding Part

When you give the robot a movie review like:

"The movie was absolutely thrilling!"

The Encoder is the part that reads and understands this sentence.
It turns the words into numbers (because the robot only understands numbers), and tries to figure out the "meaning" behind the words.

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🗣️ Decoders — The Speaking Part

Once the Encoder has understood the sentence, the Decoder helps the robot write new reviews.
It uses the knowledge from the Encoder to predict and generate the next best word, step by step, like:

"The movie was absolutely thrilling and the acting was superb."

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🔢 Vector — Words as Numbers

For the robot, words like thrilling or acting are not words, they’re numbers.
Each word becomes a Vector — which is just a fancy word for a list of numbers. For example:

thrilling → [0.5, 0.9, 0.1, ...]

This helps the robot do math to figure out word relationships.

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💎 Embeddings — Word Meaning in Numbers

Those Vectors come from something called Embeddings.

Think of Embeddings as a special way of mapping words to numbers, so words with similar meaning are placed closer together.

For example:

"Thrilling" and "Exciting" have similar numbers, meaning the robot understands they’re similar in real life too!

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🧭 Positional Encoding — Word Order Awareness

The robot also needs to know the order of the words.
Is it:

"The movie was thrilling"
or
"Thrilling the was movie"?

To help with this, the robot adds Positional Encoding — which is extra number information telling it the position of each word.

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💡 Semantic Meaning — Understanding Context

When the robot reads:

"The acting was cool."

It understands cool means good, not cold.
This ability to understand the meaning depending on the sentence is called Semantic Meaning.

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👁️‍🗨️ Self-Attention — What’s Important?

The robot looks at every word and asks:

"Which other words should I focus on to understand this better?"

In the sentence:

"The movie, although long, was absolutely thrilling."

The word thrilling might "pay attention" to although long to understand the full meaning.
This ability is called Self-Attention.

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🎯 Softmax — Making a Decision

When the robot wants to choose the next word, it uses Softmax.
This turns a list of numbers into probabilities, like:

The robot usually picks the word with the highest probability!

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🧠🧠 Multi-Head Attention — Looking in Many Ways

The robot doesn't just focus in one way.
It uses Multi-Head Attention — meaning it can look at the sentence in multiple ways at the same time:

• One head focuses on emotion.

• One head focuses on grammar.

• One head focuses on word relationships.

All this helps it understand and generate better text.

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🔥 Temperature — Controlling Creativity

When the robot generates text, you can control how "risky" or "creative" it should be.

• Low Temperature (e.g. 0.2) → Picks safe, common words. (More predictable)

• High Temperature (e.g. 1.0) → Picks more random or rare words. (More creative)

So:

• Low temp:

"The movie was great."

• High temp:

"The movie was a rollercoaster of emotions."

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📅 Knowledge Cutoff — Memory Limit

The robot was trained on lots of movie reviews, but only until a certain date.
If the last training was in 2024, it won’t know about movies released in 2025.
That’s called the Knowledge Cutoff.

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✂️ Tokenization — Breaking Down Sentences

Before doing anything, the robot first breaks the sentence into tokens — small chunks, which could be words, sub-words, or even single letters.

For example:

"thrilling" → might break into: ["thrill", "ing"]

This is how the robot turns sentences into pieces it can handle.

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📦 Vocab Size — The Robot’s Word Limit

Vocab Size is like the robot’s dictionary.
If its vocab size is 50,000, that means it knows 50,000 tokens.

If a word isn't in the vocab, the robot breaks it into smaller known pieces.

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✅ Summary:

Imagine you teach a robot to write movie reviews. It needs to: