When you type a message to ChatGPT, Claude, or another AI assistant, a remarkable sequence of events unfolds behind the scenes. Let’s break down this process step by step to understand how these Large Language Models (LLMs) transform your words into thoughtful responses.
Step 1: Your Prompt Gets Tokenized
First, the AI breaks your text into small pieces called “tokens.” These aren’t exactly words—sometimes they’re parts of words, sometimes they’re punctuation marks, and sometimes they’re common phrases.
For example, “What is artificial intelligence?” might become: [“What”, “is”, “artificial”, “intel”, “ligence”, “?”]
This is like breaking a sentence into puzzle pieces according to the AI’s predefined vocabulary.
Step 2: Tokens Become Numbers
Computers work with numbers, not words. So each token gets converted to a specific ID number based on the AI’s vocabulary dictionary.
If “What” is always token #367 and “is” is always #264, then these exact same numbers will be used whenever these tokens appear in any prompt. This step is simply translating text chunks into their corresponding ID numbers.
Step 3: Numbers Transform into Meaningful Vectors
Now comes the first truly sophisticated step. Each token ID gets converted into an “embedding vector”—a list of hundreds or thousands of numbers that represent that token in a “meaning space.”
Instead of just the number 367 for “What”, the token gets expanded into a rich vector like: [0.12, -0.34, 0.87, 0.02, -0.56, …] continuing for hundreds more numbers
These vectors contain remarkable properties:
- Words with similar meanings have similar vectors
- The vectors capture relationships (like “king” – “man” + “woman” ≈ “queen”)
- They position each word in a multidimensional space where distance represents meaning differences
This transformation from simple IDs to rich vectors is what allows the AI to work with meaning, not just symbols.
Step 4: The Attention Mechanism Analyzes Relationships
With these meaningful vectors, the AI uses its “attention mechanism” (part of the neural network) to analyze how all tokens relate to each other.
The attention component calculates a mathematical score for how each token should “pay attention” to every other token in your prompt. For example, in “The cat sat on the mat,” when processing “sat,” the AI gives high attention to “cat” (who’s doing the sitting) and less attention to “the.”
This happens across multiple “attention heads” that each look for different types of relationships (grammar, subject-verb connections, contextual references, etc.).
Step 5: Deep Neural Network Processing – The Heart of Understanding
This step is where the real “intelligence” emerges. Let’s look deeper at how embedding vectors and attention patterns work together inside the neural network.
How Embedding Vectors Capture Meaning
Embedding vectors organize words in a mathematical space where relationships between concepts are preserved as geometric relationships. This allows the AI to “understand” connections between words.
Simple Example: In this mathematical space:
- The vectors for “dog” and “puppy” would be very close to each other
- The vectors for “cat” and “kitten” would be close to each other
- “Dog” and “cat” would be moderately close (both are pets)
- “Car” would be far away from all these animal terms
The model might learn that there’s a consistent relationship between adult animals and their young. So the difference between “dog” and “puppy” vectors might be similar to the difference between “cat” and “kitten” vectors. This lets the AI understand analogies and relationships without being explicitly programmed with this knowledge.
Practical Translation Example
Here’s how embedding vectors help with language tasks:
If I said “I speak English and French, but I’m trying to learn…” the AI predicts what might follow by using embedding vectors:
- Words like “Spanish,” “Italian,” or “German” have embedding vectors in the “language” region of the vector space
- Words like “guitar,” “cooking,” or “photography” would have vectors far away from that region
- So the model will assign higher probability to language-related terms
How Attention Weaves Everything Together
Attention is the mechanism that lets the AI focus on relevant connections. For each position in your prompt, attention calculates how strongly it should “attend” to every other position.
Simple Example: For the sentence “The cat, which has white paws, is sleeping on the couch.”
- When processing “is sleeping,” attention helps the model focus strongly on “cat” (the subject doing the sleeping)
- When processing “which has white paws,” attention helps the model connect this phrase back to “cat”
- When processing “the couch,” attention helps connect to “sleeping on” to complete the location relationship
These attention patterns form a complex web of connections that changes for each word being processed.
Multiple Types of Attention
Modern AI models use multiple “attention heads” in parallel—each learning to focus on different kinds of relationships:
- Some heads might track grammatical structure
- Others might focus on topic consistency
- Others might connect subjects with their descriptions
- Others might track logical relationships
Practical Example: In the sentence “Alice told Bob that she would bring her laptop to the meeting,” different attention heads help resolve:
- Who “she” refers to (probably Alice)
- Whose laptop (probably Alice’s)
- What event is being discussed (the meeting)
Combining Embeddings and Attention
As your prompt’s tokens move through the neural network:
- Each token’s embedding vector gets updated based on attention patterns
- Early layers might handle basic patterns like grammar and simple word relationships
- Middle layers might capture more complex relationships between concepts
- Deeper layers might understand abstract themes, intentions, and nuanced meanings
For instance, when processing “I’m feeling under the weather today,” early attention patterns might connect “under” and “weather” as a phrase, while deeper layers understand this as an idiom about feeling ill, not a literal statement about being beneath weather.
Building Contextual Understanding
What makes modern LLMs so powerful is that they don’t treat words in isolation. The embedding of “bank” will be different in “river bank” versus “bank account” because attention patterns incorporate surrounding context.
As information flows through dozens of neural network layers, each token’s representation becomes increasingly refined by its context, leading to a sophisticated understanding of your entire prompt—not just word by word, but as an interconnected whole.
Step 6: Next Token Prediction
Based on all this processing, the AI calculates probability scores for what token should come next. For example, if your prompt ends with “The capital of France is,” the AI might calculate:
- “Paris”: 98% probability
- “Lyon”: 1% probability
- “Rome”: 0.1% probability
- Thousands of other possibilities with smaller probabilities
Step 7: Sampling to Select the Next Token
Rather than always picking the highest probability token, the AI uses “sampling” techniques to introduce some controlled randomness. Settings like “temperature” control how random these selections are:
- Low temperature: More predictable, focused responses
- High temperature: More creative, varied responses
Step 8: Building the Response Token by Token
After generating the first token of its response, the AI adds this to all previous tokens and repeats the entire process—embedding, attention, neural processing, prediction, and sampling—to select the next token.
This happens one token at a time, with each new token influenced by both your original prompt and all previously generated tokens in the response.
Step 9: Presenting the Final Text
Finally, the sequence of generated tokens gets converted back from numbers to text and displayed as the AI’s response to your prompt.
All of this—from receiving your prompt to generating a complete response—happens in seconds, creating what feels like a fluid conversation.
What Makes This So Remarkable?
The key insight is that the AI doesn’t have pre-written answers. Instead, it’s developed a statistical model of language by analyzing vast amounts of text and learning to represent words and concepts in a mathematical space where meaning relationships are preserved.
Through this sophisticated pattern-matching ability, the AI creates the impression of understanding, even though it lacks true comprehension or consciousness. It’s predicting what text would naturally follow your input based on all the patterns it learned during training.
Understanding this process helps us better appreciate both the impressive capabilities and fundamental limitations of today’s AI assistants.
Pattern-Matching vs. True Understanding
It’s important to recognize that despite their impressive capabilities, today’s AI systems are essentially sophisticated statistical pattern-matching machines. They’re not “understanding” text in the way humans do—with consciousness, intentions, beliefs, or experiences of the world.
What these systems are doing is predicting probabilities based on patterns in their training data. When an AI like Claude or GPT responds thoughtfully to your question about emotions, ethics, or personal experiences, it’s not drawing from actual lived experience—it’s generating text that statistically resembles how humans write about these topics.
This statistical approach can create remarkably convincing simulations of understanding without the underlying cognitive processes humans associate with comprehension. The AI has no goals, desires, beliefs, or awareness—only the ability to predict what text patterns should follow other text patterns.
The Scale of Modern LLMs
To appreciate the scale of these systems:
- Parameter Count: Large models like GPT-4 and Claude are believed to have hundreds of billions to over a trillion parameters (the adjustable values that define how the neural network processes information).
- Training Data: These models are trained on hundreds of billions to trillions of words of text, representing a significant portion of the publicly available internet, books, articles, and other written materials.
- Computing Resources: Training these models can require hundreds or thousands of specialized AI accelerator chips (like GPUs or TPUs) running for weeks or months, consuming millions of dollars worth of computing resources and electricity.
- Memory Requirements: The full versions of these models require hundreds of gigabytes of memory just to hold their parameters.
Comparison to the Human Brain
While impressive in scale, even the largest AI models pale in comparison to the human brain:
- The human brain has roughly 86 billion neurons with approximately 100 trillion synapses (connections).
- Unlike AI neural networks, which are relatively homogeneous, the brain has hundreds of different types of neurons organized into specialized regions and structures refined over millions of years of evolution.
- The brain processes multiple sensory inputs simultaneously, integrates them with memories and emotions, and coordinates physical actions—far beyond the text-only domain of LLMs.
- Perhaps most significantly, the human brain has consciousness and subjective experience—qualities that remain absent in even the most advanced AI systems today.
This perspective helps us understand both the remarkable achievements of modern AI and its fundamental limitations. These systems are extraordinary tools for pattern recognition and text generation, but they remain mathematical models of language probability rather than conscious entities with understanding.Retry