How LLM's Work
Deep diving into how LLM's work by understanding "Attention is all you need" research paper
GPT
GPT = Generative Pre-Trained Transformer
Generative - Unlike serach engine these LLM's can generate next set of SEQUENCES based on your input.Pre-trained: Fed with massive text data (books, code, etc.) to learn patterns.Transformer: The core tech that makes it all possible (more below).
1. The Foundation: Predict-the-Next-Word
How prediction looks like from abstraction, "Given a sequence of words, what's the statistically most probable next word?"
- Trained on trillions of words (books, websites, code)
We'll try to understand GenAI by comparing it with Machine Learning so that we get more clarity
| ` | Traditional ML | Generative AI |
|---|---|---|
| Goal | Analyze/predict from existing data | Create new data resembling the original |
| Input | Structured/tabular data, labeled | Text, images, video, code, speech, etc. |
| Output | Labels, scores, categories | New text, code, images, ideas, responses |
| Example | Spam detection, loan approval | ChatGPT writing an email for you |
Attention is all your need
This was a transformer model paper published by google in 2017 Link
💡 Fun fact: Google built it for language translation... but OpenAI saw its potential and used it to create GPT. The rest is history!
Now let's understand how this transformer model works
Step 1: Encoding (Turning Words into "Math")
Vector embedding
Giving Words a Digital Fingerprint. Computers don't understand human languages like English or Hindi, they only understand numbers
Imagine every word ("cat", "quantum", "😊") gets its own unique barcode of numbers.
- These barcodes capture meaning:
- Similar words (king/queen) have similar barcodes
- Relationships are preserved: King - Man + Woman ≈ Queen
- Like GPS coordinates for ideas:
"apple" = [-0.5, 1.2, 0.3] (fruit)vs."Apple" = [0.9, -0.2, 1.4] (company)
Step 2: Positional Encoding (Remembering Word Order)
Why? Because "Dog bites man" ≠ "Man bites dog"!
-
The model adds "position tags" to each word’s barcode:
[Word barcode] + [Position barcode] -
Like giving every word a timestamp:
"The" (Position 1), "cat" (Position 2), "sat" (Position 3)
Step 3: Multi head self attention
Here is where the model truly starts to understand context
When reading a word, the model asks itself:
"Which other words matter most to understand this word?"
Example: Understanding "it" in a sentence
- Sentence: "The cat chased the ball because it was playful."
- A human knows that “it” refers to the cat, not the ball.
- The model figures this out by paying attention to the right words.
Enter: Self-Attention This mechanism lets the model look at all the other words in the sentence and decide how much each one matters for understanding the current word.
In this case, "it" attends more to "cat" than "ball" — just like we would!
"Multi-Head" = Multiple perspectives:
Like a team of detectives examining the sentence differently:
- Detective 1 focuses on grammar (verbs/nouns)
- Detective 2 tracks pronouns (it/they)
- Detective 3 analyzes emotions (playful/angry)
Self-Attention visual:
The cat [sat] on the mat because it was tired
│ └───────┘ │ │ ▲ │
└─────────────────────┴───────────┴──────────┘ │
"it" pays most attention to "cat" and "sat" │
"tired" connects strongly to "cat" and "sat" ◄──────┘ Multi-Head Self-Attention is how the model sees every word from multiple angles at once — like a super attentive reader with many minds working together.
Step 4: Bringing It All Together
After attention, the enriched word-data flows through:
- Feed-Forward Networks: "Digesting" relationships
- Layer Normalization: Keeping calculations stable
- Residual Connections: Preserving original meaning
This repeats across 12-100+ layers—each adding deeper understanding!
Why This Matters to YOU
- You don’t need to build this: Just like you don’t build processors to use a laptop!
- Your superpower: Leveraging these pre-built "language engines" (GPT-4, Claude, etc.) to:
- Create content using these model
- Use the LLM's intelligence to analyze data
- Build AI assistants
- Solve niche problems (e.g., medical reports, legal docs)
🔑 Key insight: Transformers are context-aware pattern machines. They don’t "know" facts—they predict what words likely come next based on patterns from training data.