How Do LLMs Work?

LLMs work through a sophisticated process of breaking down text into tokens, processing them through multiple neural network layers using self-attention mechanisms, and predicting the most likely next token in a sequence. This simple-sounding mechanism, repeated billions of times, enables remarkably intelligent language understanding and generation.

The High-Level Process

At the highest level, LLMs operate in distinct phases:

Input → Tokenization → Embedding → Processing → Probability → Output

Each phase is crucial to understanding how LLMs transform text input into intelligent responses.

Step 1: Tokenization

LLMs don't process text character-by-character or word-by-word. Instead, they use tokens—typically subword units:

Why Tokens?

Words are too large (vocabulary explosion)
Characters are too small (context lost)
Tokens strike optimal balance
Typically 1,000-100,000 token vocabulary

How Tokenization Works:

Common words: "cat" = single token
Uncommon words: "unbelievable" = multiple tokens ["un", "believ", "able"]
Numbers, punctuation: Treated as separate tokens
Special tokens: [START], [END], [PAD] for control

Example:

Text: "The quick brown fox"
Tokens: ["The", " quick", " brown", " fox"]
Token IDs: [1, 2043, 1853, 4013]

Step 2: Embedding

Once tokenized, each token becomes a numerical vector:

What is an Embedding?

Vector of numbers representing token meaning
Typically 768 to 4,096 dimensions
Similar words have similar vectors
Learned during training

Example (Simplified):

Token: "cat"
Embedding: [0.2, -0.5, 0.8, 0.1, ..., -0.3]
           (actual: 768+ dimensions)

Why Embeddings?

Convert text to numbers (required for math)
Capture semantic meaning
Enable mathematical operations
Learned representations improve with training

Position Encoding:

Adds information about token position
Preserves word order
Without this, model wouldn't understand sequence
Added to embeddings before processing

Step 3: Transformer Processing

This is the core of modern LLMs—the Transformer architecture:

Self-Attention Mechanism:

Self-attention is the breakthrough that makes transformers work:

Query, Key, Value Computation:
- Convert embeddings to Query (Q), Key (K), Value (V) vectors
- Each uses different linear transformation
- Multiple attention "heads" do this in parallel
Attention Scores:
- Compare Query with all Keys
- Higher score = more attention to that token
- Formula: Attention(Q, K, V) = softmax(QK^T / √d_k) V
Weighting and Combining:
- Higher scores → more weight
- Lower scores → less weight
- Weighted sum of Values produces output
- Each position attends to all other positions

What Self-Attention Does:

Learns which tokens are related
Long-distance relationships captured
Multiple heads learn different patterns
Enables understanding context across entire input

Example:

Sentence: "The bank is near the river"
Attention Pattern:
- "bank" attends heavily to "river" (disambiguates meaning)
- "The" attends to nouns
- "is" attends to subject and complement

Feed-Forward Network:

After attention, each token processed independently
Two linear layers with non-linear activation
Adds depth and model capacity
Applied to each position

Layer Stacking:

Modern LLMs have 50-200+ transformer layers
Each layer refines understanding
Early layers: Simple patterns (syntax)
Deep layers: Complex concepts (semantics)
Information flows bottom to top

Normalization:

Layer normalization stabilizes training
Applied before and after sub-layers
Helps gradient flow during backpropagation
Improves training stability

Step 4: Output Probability Calculation

After processing through all layers:

Linear Projection:

Transform final token representation to vocabulary size
One score per possible token
Example: For 50,000 vocab, 50,000 scores

Softmax Function:

Convert scores to probability distribution
All probabilities sum to 1.0
Formula: P(token) = e^(score) / sum(e^(all_scores))
Higher scores → higher probability

Resulting Distribution:

"The quick brown fox jumps over the"
→ Model predicts next token:
   "lazy" - 0.35 probability
   "dog" - 0.25 probability
   "cat" - 0.15 probability
   "wall" - 0.08 probability
   (other tokens) - 0.17 probability

Step 5: Token Selection and Output

Selecting the Next Token:

Multiple strategies available:

Greedy Selection:

Choose highest probability token
"lazy" (0.35) always selected
Deterministic, consistent
Can be repetitive

Temperature Sampling:

Control randomness with temperature parameter
Higher temp (>1): More random, diverse
Lower temp (<1): More confident, deterministic
Temperature=1: Use probabilities as-is

Top-K Sampling:

Consider only top K most likely tokens
Ignore very low probability options
Reduces hallucination
Balances quality and diversity

Top-P (Nucleus) Sampling:

Include tokens until probability mass reaches threshold
More sophisticated than top-K
Adapts to probability distribution shape
Popular in modern models

Step 6: Iterative Generation

One token isn't enough—generation continues:

Process Repeats:

Add predicted token to input
Re-process through all layers
Get new probability distribution
Select next token
Repeat until:
- Reaching max length, or
- Generating [END] token, or
- User stops generation

Example Generation Sequence:

Input: "The capital of France is"
Step 1: Predict → "Paris"
Context: "The capital of France is Paris"

Step 2: Predict → "a"
Context: "The capital of France is Paris a"

Step 3: Predict → "beautiful"
Context: "The capital of France is Paris a beautiful"
...continues...

This is why LLMs are slow:

Generate one token at a time
Each token requires full forward pass
No parallelization in generation
Large models can take seconds per response

Full Architecture Visualization

Input Text
    ↓
Tokenization
    ↓
Embedding (add position info)
    ↓
[Transformer Block 1]
  - Self-Attention (multi-head)
  - Feed-Forward Network
  - Layer Norm
    ↓
[Transformer Block 2]
  (same as Block 1)
    ↓
... (50-200+ blocks) ...
    ↓
[Output Layer]
  - Linear projection to vocab size
  - Softmax to get probabilities
    ↓
Token Selection
    ↓
Output Token
    ↓
Add to context, repeat

Why This Architecture Works

Self-Attention Advantages:

Captures long-range dependencies
Learns what's important to attend to
Highly parallelizable
Works with variable-length sequences

Transformer Strengths:

Efficient training (parallel processing)
Effective at learning language patterns
Scales well (more layers, more parameters)
Learns diverse language phenomena

Emergent Benefits:

Language understanding emerges
Reasoning abilities develop
Few-shot learning becomes possible
Transfer to new tasks works

What Gets Learned During Training?

Layer-by-Layer Learning:

Early Layers (1-5):

Syntax and grammar
Character patterns
Part-of-speech information
Word similarities

Middle Layers (10-50):

Semantic relationships
Conceptual groupings
Abstract patterns
Discourse structure

Late Layers (50+):

High-level reasoning
Task-specific knowledge
Fact encoding
Complex relationships

This is why fine-tuning works:

Layers already understand language
Only top layers need adjustment
Faster and cheaper than pre-training
Preserves learned knowledge

Common Misconceptions Clarified

"LLMs understand language like humans"

Reality: LLMs are sophisticated pattern matchers
Understanding through learned statistical patterns
Not conscious or aware
Effective pattern recognition, not true comprehension

"Attention mechanism is like human attention"

Reality: Mathematically similar but not identical
Humans can't sustain attention to all inputs simultaneously
LLMs attend to all tokens in parallel
Loosely inspired by human attention

"LLMs follow explicit rules"

Reality: No hand-coded rules
Learned patterns from training data
Implicit, distributed across parameters
Can't be easily extracted or audited

"Bigger model always means smarter"

Reality: Size is important but not everything
Data quality matters
Training approach matters
Efficiency gains through better architecture

Training vs. Inference

Training Process:

Shows model many examples
Calculates prediction error
Adjusts weights to reduce error
Repeated billions of times
Slow, expensive (months, billions of dollars)

Inference Process:

Model weights fixed
Process new input through learned weights
One forward pass per token
Fast-ish (milliseconds to seconds)
Runs on GPUs or TPUs

GPU Acceleration

LLMs require GPUs because:

Massive Parallelization:

Matrix operations parallelizable
GPUs excel at parallel computation
CPUs can't keep up

Specialized Operations:

Tensor operations optimized for GPUs
Custom CUDA kernels
Tensor cores for matrix multiplication

Memory Bandwidth:

GPUs have much higher memory bandwidth
Critical for large models
CPU bandwidth becomes bottleneck

Organizations leverage cloud GPU platforms like E2E Networks providing NVIDIA A100 and H100 for both training and inference of LLMs.

Frequently Asked Questions

How does the model know what to generate? It doesn't "know" in human sense. It calculates probability of each token given training patterns. High-probability tokens are selected based on statistical likelihood.

Why are LLMs slow at generating text? One token at a time is fundamental to the design. Each token requires full forward pass through all layers. Optimizations exist but can't eliminate this.

What's the difference between training and using an LLM? Training: Adjust weights to learn patterns (months, expensive). Using: Fixed weights, generate text (fast, cheap per inference).

Why do LLMs sometimes repeat themselves? Greedy sampling picks most likely token repeatedly. High-probability tokens dominate. Sampling strategies (temperature, top-K) mitigate this.

Can you extract knowledge from an LLM? Not easily. Knowledge is distributed across parameters. No mechanism to extract rules or facts directly. Black box nature is a limitation.