2.1 ChatGPT: How It Works - From Prompt to Response

When you type a question into ChatGPT and receive a thoughtful, well-written answer within seconds, it feels like magic. But behind this seemingly simple interaction lies one of the most sophisticated engineering achievements of our time. This article will take you on a detailed journey through every step of ChatGPT's operation, using clear analogies and avoiding complex mathematics. By the end, you'll not only understand how it works but also appreciate the remarkable engineering that makes it possible.

The Complete Pipeline: From Your Words to AI Response

ChatGPT's response generation can be broken down into five distinct phases, each with its own fascinating mechanics:

Phase 1: Tokenization - The Digital Vocabulary

When you type "Explain quantum computing simply," ChatGPT doesn't see English words. It sees numbers. The first step is tokenization—breaking your text into meaningful chunks called tokens.

Tokens aren't always whole words. They can be:

• Whole words: "the", "computer", "science"
• Word parts: "un" + "believable", "play" + "ing"
• Punctuation: ".", "!", "?"
• Special characters: "\n" (new line), spaces in some cases

ChatGPT's vocabulary contains approximately 50,000 tokens. This approach is efficient because:

1. Common words get their own tokens ("the" = token 464)
2. Rare words can be built from subword tokens
3. It handles multiple languages within the same system
4. It can represent words not in the original training data

Your sentence "Explain quantum computing simply" might be tokenized as:
["Explain", " quantum", " computing", " simply"] → [3301, 4235, 6789, 1250]

Phase 2: Embedding - Words as Vectors in Meaning Space

Once tokenized, each token gets converted into an embedding vector—a list of 1,536 numbers (for GPT-3) that represents its meaning in a high-dimensional space.

Think of this like a cosmic library where:

• Each word has coordinates in a 1,536-dimensional space
• Words with similar meanings are close together
• Semantic relationships are preserved: "king" - "man" + "woman" ≈ "queen"
• Syntactic relationships: "run" and "running" have related vectors

The embedding process captures astonishing linguistic relationships:

Relationship Type	Example	Vector Relationship
Gender	king - man + woman	≈ queen
Verb tense	run + ing	≈ running
Country-capital	France - Paris + Tokyo	≈ Japan
Analogies	teacher : student :: doctor : ?	≈ patient

These embeddings aren't programmed—they're learned during training by analyzing billions of sentences. The model discovers that words appearing in similar contexts should have similar embeddings.

Phase 3: Transformer Processing - The Attention Revolution

This is where the transformer architecture shines. Your embedded tokens pass through 96 layers (in GPT-3) of neural processing, each applying two key operations:

1. Self-Attention Mechanism: This is ChatGPT's "spotlight of focus." For each word, it calculates how much attention to pay to every other word in the sentence. In "The cat sat on the mat," when processing "sat," the attention mechanism learns to focus more on "cat" (who did the sitting) and "mat" (where they sat), less on "the."

2. Feed-Forward Networks: After attention, each token's representation passes through a neural network that transforms it, potentially combining information from different parts of the sentence.

This layered processing creates increasingly abstract representations:

• Early layers: Recognize basic syntax and grammar patterns
• Middle layers: Understand sentence structure and basic semantics
• Later layers: Capture complex meaning, tone, and context
• Final layers: Prepare for next-token prediction with contextual understanding

Phase 4: Autoregressive Generation - The Word-by-Word Dance

Now comes the generation phase. ChatGPT builds responses one token at a time, with each new token influenced by all previous tokens. This is called autoregressive generation.

Let's trace generating the response to "Why is the sky blue?":

1. Step 0: Input: "Why is the sky blue?" [END]
2. Step 1: Model predicts first token: "The" (probability: 68%)
3. Step 2: Input becomes: "Why is the sky blue? The"
4. Step 3: Predicts: "sky" (75%)
5. Step 4: Input: "Why is the sky blue? The sky"
6. Step 5: Predicts: "appears" (62%)
7. ... and so on until generating [END] token

At each step, the model produces a probability distribution over all 50,000 possible tokens. The actual selection uses techniques like:

• Temperature sampling: Adjusts randomness (more on this later)
• Top-p sampling: Considers only the most probable tokens
• Repetition penalty: Discourages repeating the same phrases
• Length penalty: Encourages appropriate response length

The Mathematics Behind the Magic (Simplified)

While we're avoiding complex math, understanding the basic principles helps appreciate the engineering:

Attention Formula (Simplified):
Attention = Softmax(Q × K^T / √d) × V

Where:

• Q (Query): "What am I looking for?"
• K (Key): "What information do I have?"
• V (Value): "What is that information worth?"
• d: Dimension size for scaling

This attention mechanism allows ChatGPT to weigh the importance of every word relative to every other word, creating rich contextual understanding.

The Training Process That Enabled This Capability

ChatGPT's remarkable abilities come from its extensive training, which occurred in three meticulously designed phases:

Phase 1: Pre-training - The Foundation of Knowledge

For months, thousands of powerful GPUs processed approximately 45 terabytes of text data—equivalent to millions of books. The training objective was simple but powerful: predict the next word in a sentence.

Key training data sources included:

• Books (fiction and non-fiction): 22% of training data
• Web pages (filtered for quality): 60% of data
• Wikipedia: 8% of data
• Academic papers and code repositories: 10% of data

During this phase, the model learned:

• Grammar and syntax across multiple languages
• World knowledge from factual texts
• Reasoning patterns from logical arguments
• Stylistic variations across genres
• Code syntax and programming patterns

Phase 2: Supervised Fine-Tuning - Learning to Converse

After pre-training, the model could generate text but wasn't yet good at conversation. Human AI trainers created thousands of dialogue examples, playing both user and assistant roles. This taught the model:

• How to follow specific instructions
• When to admit lack of knowledge
• How to maintain conversation context
• Appropriate tone and formality levels
• How to ask clarifying questions

Phase 3: Reinforcement Learning from Human Feedback (RLHF) - Alignment

This final phase made ChatGPT helpful, harmless, and honest. The process:

1. Human trainers rank multiple responses to the same prompt
2. A separate "reward model" learns to predict human preferences
3. The main model is fine-tuned using this reward model as guidance
4. The process iterates multiple times for refinement

RLHF is why ChatGPT refuses harmful requests, admits mistakes, and generally tries to be helpful rather than just accurate.

Temperature and Sampling: Controlling Creativity

The "temperature" setting fundamentally changes how ChatGPT generates text:

Temperature	Effect on Probability Distribution	Use Cases	Example Behavior
0.0 (Deterministic)	Always picks highest probability token	Code generation, factual responses	Consistent but potentially repetitive
0.2-0.5 (Low)	Slight randomness, favors high-probability tokens	Technical writing, business communication	Reliable with minor variations
0.7-0.9 (Medium)	Balanced exploration of possibilities	Creative writing, brainstorming	Interesting but coherent
1.0-1.5 (High)	High randomness, explores unlikely tokens	Poetry, experimental fiction	Surprising, sometimes nonsensical

Other sampling techniques include:

• Top-p (nucleus sampling): Only considers tokens whose cumulative probability exceeds p (e.g., 0.9)
• Top-k: Only considers the k most probable tokens (e.g., top 40)
• Beam search: Considers multiple possible sequences simultaneously

Context Window and Memory Management

ChatGPT maintains a context window of 4096 tokens (approximately 3000 words). This isn't like human memory—it's more like having a fixed-size notepad where:

• New tokens are added to the end
• Oldest tokens drop off when limit is reached
• The entire context is reprocessed with each new token
• No information persists between separate conversations

This explains why:

• ChatGPT can reference earlier parts of a conversation
• Very long conversations cause it to "forget" the beginning
• Each conversation starts fresh with no memory of past interactions
• Context management is crucial for coherent long-form generation

Limitations and Their Technical Explanations

Understanding ChatGPT's architecture explains its limitations:

Limitation	Technical Explanation	Workaround
Hallucinations	Pattern completion without fact verification; statistical generation of plausible-sounding text	Ask for sources, fact-check critical information
No real-time knowledge	Training data cutoff (Jan 2022 for GPT-3.5); no internet access in basic version	Use plugins or paid versions with web access
Mathematical errors	Pattern-based rather than algorithmic calculation; no built-in calculator	Ask it to reason step-by-step or use code interpreter
Inconsistent responses	Probabilistic generation; different temperature/sampling settings	Use lower temperature, be more specific in prompts
No true understanding	Statistical pattern matching without consciousness or world experience	Treat as advanced tool, not conscious entity

Putting It All Together: Complete Walkthrough Example

Let's trace the complete process for: "Explain blockchain to a 10-year-old"

1. Tokenization: ["Explain", " blockchain", " to", " a", " 10", "-", "year", "-", "old"] → [4231, 8950, 12, 5, 112, 45, 678, 45, 234]
2. Embedding: Each token becomes a 1536-dimensional vector
3. Context Processing: 96 layers analyze relationships:
   • Layer 5: Recognizes "explain" requires simplified language
   • Layer 25: Identifies "blockchain" as technical concept
   • Layer 50: Understands "10-year-old" means child-friendly explanation
   • Layer 75: Prepares explanatory structure pattern
   • Layer 96: Ready for generation with appropriate tone
4. Generation: Autoregressive token-by-token generation:
   • Token 1: "Imagine" (probability 72%)
   • Token 2: "a" (85%)
   • Token 3: "digital" (68%)
   • ... continues for 150 tokens...
   • Final token: [END] (91%)
5. Output: "Imagine a digital Lego chain where everyone has the same copy..."

The entire process completes in under 2 seconds, performing approximately 100 billion calculations.

The Future of Language Model Architecture

Current research is pushing beyond the transformer architecture with innovations like:

• Mixture of Experts: Different parts of the network specialize in different domains
• Sparse Attention: More efficient attention mechanisms for longer contexts
• Multimodal Models: Processing text, images, and audio together
• Retrieval-Augmented Generation: Combining generation with external knowledge lookup
• Chain-of-Thought: Explicit reasoning steps before final answer

This deep technical understanding should help you appreciate both the remarkable capabilities and inherent limitations of ChatGPT. It's not magic—it's mathematics, engineering, and pattern recognition operating at a scale beyond human comprehension yet accessible through simple conversation.

In our next article, we'll explore how similar transformer principles are adapted for image generation in Midjourney and Stable Diffusion. The leap from predicting next words to generating coherent images represents another fascinating chapter in AI's evolution.