1.1. Understand Tokenization, Context Windows, and the Attention Mechanism
🪄 Step 1: Intuition & Motivation
Core Idea: Before a language model can “think” or “reason,” it first needs to understand text as numbers. Computers can’t read words like “banana” or “wisdom” — they see only patterns of numbers. Tokenization and attention are how we teach the model to understand relationships between those numbers, like how words relate to each other in meaning or order.
Simple Analogy: Imagine you’re at a big party. Every guest (word) is talking, and the model needs to figure out who’s speaking to whom and what’s important. Tokenization gives everyone a name tag (a numeric ID), and attention decides who listens carefully to which guest.
🌱 Step 2: Core Concept
Let’s gently unpack this into three smaller pieces — Tokenization, Context Windows, and Attention.
1️⃣ Tokenization — Turning Words into Numbers
Every LLM starts by breaking your sentence into tokens — small text pieces like words or sub-words.
For example:
- Sentence: “Learning is powerful.”
- Tokens (simplified):
[Learning, is, power, ##ful, .]
Each token is assigned a numeric ID from a vocabulary, and those IDs are turned into vectors — little bundles of numbers that capture meaning.
These embeddings let similar words have similar numerical patterns — so “cat” and “dog” end up close in vector space, while “quantum” sits far away.
2️⃣ Context Windows — How Much the Model Can ‘Remember’
LLMs don’t have infinite memory. They can only “see” a fixed number of tokens at once — this is called the context window.
If the model’s context window is 8K tokens, it can only consider roughly 8,000 tokens from the conversation or document. Anything before that is forgotten — just like when you can’t recall the start of a long novel if you haven’t bookmarked it.
This limit affects reasoning. A model might “forget” earlier details or instructions if they fall outside this window.
3️⃣ Attention — The Model’s Focus Mechanism
Now imagine every token wants to know how much attention to pay to every other token.
That’s what self-attention does: for every token, it asks,
“Which other tokens are relevant to me right now?”
The model computes “attention scores” to decide which words influence each other more. This allows it to capture relationships like:
- “not” → flips meaning of the next word
- “Paris” ↔ “France” → strong association
- “it” → refers to an earlier noun
In short: attention = smart focus.
Why It Works This Way
Language is full of dependencies — “She gave him the book that he wanted.” Without attention, a model would process this sentence word by word, like reading with blinders on.
Attention lets each word “peek” at others, even far apart ones, to understand how meaning connects across distance.
How It Fits in ML Thinking
Tokenization = preprocessing (turning raw input into structured data). Attention = feature interaction (learning relationships dynamically).
Together, they make transformers powerful enough to handle reasoning tasks, summaries, dialogue, and even planning — because they model relationships, not just words.
📐 Step 3: Mathematical Foundation
Self-Attention Formula
The core mechanism of focus is described by:
$Attention(Q, K, V) = softmax\left(\frac{QK^T}{\sqrt{d_k}}\right)V$
- Q (Query): What each token is asking (e.g., “Who’s relevant to me?”)
- K (Key): What each token offers (e.g., “I represent this concept.”)
- V (Value): The information each token carries (e.g., its meaning vector)
- $d_k$: A scaling factor — it keeps numbers stable during computation.
Each token compares its query (Q) to all other tokens’ keys (K) to calculate relevance. The softmax step then converts those comparisons into probabilities — like attention weights — before blending the corresponding values (V) together.
🧠 Step 4: Key Ideas & Assumptions
- Tokens can be broken down into consistent, reversible pieces of text.
- The model treats relationships between all tokens as potentially meaningful — no fixed order bias.
- Each word’s importance is dynamically learned through attention, not predefined.
These assumptions let the model adapt flexibly to different sentence structures and languages.
⚖️ Step 5: Strengths, Limitations & Trade-offs
✅ Strengths:
- Captures long-range dependencies (e.g., subject–verb connections far apart).
- Learns contextual meanings dynamically.
- Enables parallel processing — faster than RNNs or LSTMs.
⚠️ Limitations:
- Context window is finite — long texts can’t all fit in memory.
- Attention computation scales quadratically — expensive for long inputs.
- Forgetting happens beyond the window size.
⚖️ Trade-offs:
- Bigger context windows improve reasoning but raise compute cost.
- Sparse or linear attention models reduce cost but may miss subtle relationships.
- Scaling to 100K+ tokens often needs hybrid retrieval or compression tricks.
🚧 Step 6: Common Misunderstandings
🚨 Common Misunderstandings (Click to Expand)
- “Attention means memory.” → Not exactly; it’s focus, not storage. Once the model’s context is full, old tokens are forgotten.
- “More tokens = better understanding.” → Not always; after a point, noise and saturation reduce quality.
- “Self-attention is interpretability.” → It helps visualize focus, but doesn’t directly explain why a model reasons a certain way.
🧩 Step 7: Mini Summary
🧠 What You Learned: How LLMs turn text into tokens, focus on relationships using attention, and reason within a limited context window.
⚙️ How It Works: Each token computes how much to “listen” to others via attention scores, forming a meaning-aware representation.
🎯 Why It Matters: This mechanism — self-attention — is the engine that makes reasoning, coherence, and contextual understanding possible in transformers.