How does ChatGPT "understand" a question and answers it? How fast? How scary?

June 2, 2025

Breaking down ChatGPT's "dark magic" step by step, milliseconds at a time. Read on. This is super scary.

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🧠 Step 1: Input – User Types a Question

This text input becomes the input or "stimulus" for ChatGPT to initiate a sequence of events that will produce an output in the form of a human-like response.

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🔍 Step 2: Tokenization – Breaking Text into Chunks

There's no magic; only work. The key to produce "magical" work is to perform multiple mini tasks behind the scenes to present what appears to be magical output. To this, the key is to break up big tasks into mini chunks of work. ChatGPT breaks the input question into smaller pieces called tokens. Tokens are words, subwords, or characters. For example:

• "How does ChatGPT work?" becomes:

  ["How", " does", " Chat", "GPT", " work", "?"]
  

This allows the natural language processing (NLP) model to process text input applying human-like contextual differentiation.

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🧩 Step 3: Context – Analyzing The Prompt

The model interprets the meaning and intent behind the question by considering:

• The sequence and relationships between tokens.

• The relational weight (context) from the entire conversation (if there is one).

• Grammar, phrasing, tone, and even subtle nuances.

The model uses statistics to interpret how language is typically used in human conversations in similar situations. Remember that language models are trained on billions or human conversations to which they have applied statistical correlations to decipher meaning. The model's answers have been adjusted and corrected millions of times allowing the model to learn from training and experience. 

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🧠 Step 4: Arithmetics – Embedding or Turning Words into Numbers

Now each token is turned into a vector—a dimensional numerical representation. This allows the model to mathematically compare concepts like:

• "cat" being mathematically closer to "kitten" than to "car" even though the words themselves are closer in characters.

• "How does" is a common start to an question seeking an explanation (explanatory query)

The vectors are then processed by layers of transformers—mathematical functions that simulate different levels of attention and association across the whole input to make a prediction about intended meaning.

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🕸️ Step 5: Relevancy – Focusing on What Matters

ChatGPT uses a mechanism called self-attention to weigh which words are most important in the context of your input. For example:

• In “How does ChatGPT understand a question?”, more weight is given to "ChatGPT", "understand", "question" than to "how", "does", and "a".

This helps the model maintain a coherent or human-like contextual relevance when generating responses.

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🧮 Step 6: Prediction – One Word at a Time

Now the model finally begins to generate a response placing one token at a time, by:

• Predicting the most likely next word after another based on the input question and the training.

• Using its 100+ billion learned parameters (based on patterns from books, articles, code, conversations, etc.), ChatGPT can create the "magical" responses in a human-like fashion.

Example:

• Predicts "ChatGPT"

• Then "uses"

• Then "a"

• Then "transformer-based"

• And so on…

It continues until it hits a stop condition (like a period, newline, or max token count).

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🧰 Step 7: Optimization (In Some Versions)

In ChatGPT, especially in the consumer version, OpenAI applies post-processing filters, reinforcement learning from human feedback (RLHF), and safety checks to:

• Reduce harmful or misleading content.

• Maintain factual accuracy (as much as possible).

• Prioritize helpful, polite, and clear responses.

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💡 Step 8: Output - Response Displayed 

Once the full output is generated, it's returned as output representing a complete, well-organized, natural-sounding answer.

💡 Step 9: Learning  

The model can continue learning from experience if programmed to do so.

⚙️ Summary (IT CARPOOL)

1. Input → User asks a question

2. Tokenization → It’s broken into pieces

3. Context → Model determines meaning and intent

4. Arithmetics → Embedding to convert words to numerical meaning

5. Relevancy → Attention mechanism focusing on most relevant parts of input

6. Prediction → Generates the response one token at a time

7. Optimization → Optional filtering to apply safeguards and human-tuned optimization

8. Output → You get the answer on your screen

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⚡ Step-by-Step Timing Breakdown

Step	Description	Time Taken
1. Input	You type and submit a question.	User-dependent
2. Tokenization	Text is split into tokens.	~1–5 milliseconds
3. Context Understanding	Analyzes syntax and meaning.	~5–20 ms
4. Embedding	Tokens converted to vectors.	~5–10 ms
5. Attention Mechanism	Model decides what parts of the input to focus on.	~10–50 ms
6. Prediction (Decoding)	Generates response word-by-word (token-by-token).	~50–500+ ms depending on length (e.g., ~10 ms per token)
7. Filtering & Optimization	Applies safety filters and human-guided preferences.	~10–100 ms
8. Output	Text is streamed back to your screen.	~10–50 ms (streaming may overlap with earlier steps)

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⏱️ Total Estimated Time

• Short prompts with short answers: ~300–700 milliseconds

• Longer prompts and responses: 1–3 seconds

• Heavy-load times or complex responses: up to 5–10 seconds

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⚙️ Notes

• These times reflect server-side processing on OpenAI’s infrastructure, using highly optimized GPUs/TPUs.

• The "prediction" step is the most computationally expensive, especially if you're asking for long, thoughtful answers.

• Steps often overlap thanks to parallel computing and streaming output, making the experience feel faster.

🧠💻 COMPARISON: Humans vs. GPTs

1. Information Storage (Data Capacity)

System	Estimated Data Capacity
Human Brain	~100 terabytes to 2.5 petabytes (1 petabyte = 1,000 terabytes)
ChatGPT-4.0 (GPT-4-turbo)	Trained on 1.5–2+ million gigabytes (1.5–2+ petabytes) of text

✅ Verdict: GPTs

• Although the amount of data is comparable as of June 2025, GPTs continue improving at incredible speeds while the human brain is essentially stuck. Evolution takes millions of years. GPTs take milliseconds. 

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2. Processing Speed

System	Estimate
Human Brain	~1,000 Hz (neurons fire up to ~1,000 times/sec) with ~100 billion neurons
ChatGPT-4.0	Up to 100 trillion operations per second (using GPUs/TPUs in parallel on high-end clusters)

✅ Verdict: GPTs

• GPT-4.0 is already in June 2025 millions of times faster than the human brain at processing structured language queries.

• Imagine the future versions of GPTs as the years go by. They will be trillions of times faster than all humans combined. 

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3. Input/Output Bandwidth

Metric	Human Brain	ChatGPT-4.0
Input (reading/listening)	~100 bits/sec (language only)	~50,000+ tokens/minute (~400,000+ characters)
Output (speaking/writing)	~100 bits/sec	~30–60 tokens/sec

✅ Verdict: GPTs

• ChatGPT-4.0 can read and process thousands of words nearly instantly, while humans take minutes or hours if not days. Many humans need to "sleep on it" to learn while GPTs never sleep and continue learning.

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4. Learning Speed

Metric	Human Brain	GPT-4
Time to learn a new concept	Seconds to decades	Hours to weeks (during training)
On-the-fly learning	Yes	❌ No*

*ChatGPT does not "learn" from individual users after training—unless fine-tuned or updated separately.

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5. Parallelism

Capability	Human Brain	GPT-4.0
Native parallelism	Massive (100B neurons, trillions of connections)	Also massive (tens of thousands of GPUs/TPUs in parallel)
Type	Biological, analog + digital, energy-efficient	Digital, energy-hungry, but extremely fast

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🏁 Verdict: GPTs

• GPT-4.0 processes language millions of times faster than a human brain can read or write.

• It stores and accesses more raw text data than any human mind ever could.

• The human brain remains superior in sensory integration, self-awareness, and adaptive reasoning. Those of course are the next frontiers of AI and will be reached and breached in a few years.

If you're not practically terrified about AI you're clueless about AI. Now, the practical aspect of being practically terrified is not been really terrified even in the face of the most terrifying technological disruption in the history of planet Earth and life in this neck of the Milky Way woods. 

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