ChatGPT and Generative AI – How It Works

Introduction

The year 2025 continues to witness rapid advancements in generative AI technologies, with models like ChatGPT, Claude, Gemini, and LLaMA reshaping how we interact with machines. These models, powered by large-scale neural networks and trained on massive datasets, can generate human-like text, code, music, images, and even video.
OpenAI’s ChatGPT, built on GPT-4 and beyond, has become a mainstream AI assistant—used for writing, brainstorming, coding, tutoring, and more. But how does it work under the hood? What makes it “generative”? What’s the role of transformers, tokens, and training data?
This MCQ set explores the inner mechanics of ChatGPT and the broader generative AI field. Whether you’re a curious user or a budding ML enthusiast, this is a great place to test your understanding.

Quick Info: | 🟢 Beginner | ⏱️ 30 Minutes | ❓ 25 Questions

What you’ll learn

✔️ Understand how ChatGPT operates, including the use of transformers, tokens, and the training data that is used to train ChatGPT.
✔️ Explore what make the modern AI models like Claude, Gemini, and LLaMA generative.

👉 Start the ChatGPT and Generative AI – How It Works Quiz

ChatGPT and Generative AI – How It Works

1 / 25

Question 1. What does GPT stand for in ChatGPT?

A. Guided Prompt Transformer

B. General Purpose Text

C. Generative Pre-trained Transformer

D. General Processing Tokenizer

2 / 25

Question 2. Which neural network architecture is used in GPT models?

A. CNN

B. RNN

C. Transformer

D. LSTM

3 / 25

Question 3. What technique allows transformers to "attend" to different parts of a sentence?

A. Pooling

B. Dropout

C. Self-attention

D. Normalization

4 / 25

Question 4. What are tokens in LLMs like ChatGPT?

A. API endpoints

B. Pieces of text (words, subwords, or characters)

C. Database entries

D. Model parameters

5 / 25

Question 5. What is the role of a tokenizer in an LLM?

A. Manage session states

B. Run prompts

C. Convert text into numerical input for models

D. Reduce file size

6 / 25

Question 6. What does "pre-training" involve in generative AI?

A. Model compression

B. Real-time training with users

C. Training on large-scale general data before fine-tuning

D. Generating prompts

7 / 25

Question 7. What kind of AI is ChatGPT considered?

A. Narrow AI

B. Generative AI

C. Symbolic AI

D. Conscious AI

8 / 25

Question 8. Which company developed ChatGPT?

A. Google

B. OpenAI

C. Meta

D. Anthropic

9 / 25

Question 9. Which model is used in ChatGPT Pro (as of 2025)?

A. GPT-3

B. GPT-3.5

C. GPT-4

D. GPT-4-turbo

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Question 10. What is the primary function of ChatGPT?

A. Execute code

B. Predict and generate human-like text

C. Translate images

D. Sort data

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Question 11. Which is a limitation of LLMs like ChatGPT?

A. They can generate incorrect or biased content

B. They can't process text

C. They require human supervision to operate

D. They only work offline

12 / 25

Question 12. What does "temperature" control in text generation?

A. Sentiment of the response

B. Randomness and creativity of outputs

C. Model's accuracy

D. API speed

13 / 25

Question 13. Which key component enables GPT models to generate coherent long passages?

A. Pooling layers

B. Convolution

C. Context window (attention span)

D. Dropout layer

14 / 25

Question 14. What does fine-tuning mean in AI models?

A. Reducing file size

B. Additional training on specific data

C. Removing bias

D. Changing activation functions

15 / 25

Question 15. Which of the following is NOT a generative AI model?

A. DALL·E

B. Claude

C. ResNet

D. Gemini

16 / 25

Question 16. Which model is designed specifically for image generation?

A. GPT-4

B. DALL·E

C. BERT

D. LLaMA

17 / 25

Question 17. What is "prompt engineering"?

A. Writing code for models

B. Crafting inputs to get better outputs from models

C. Training models on larger datasets

D. Calibrating model weights

18 / 25

Question 18. Which company built Claude, a major ChatGPT competitor?

A. Meta

B. Google

C. Anthropic

D. DeepMind

19 / 25

Question 19. What is an example of "multimodal AI"?

A. Text-to-text generation only

B. AI that can process both text and images

C. AI that only runs locally

D. Any supervised learning model

20 / 25

Question 20. What's the term for adjusting model outputs to align with human preferences?

A. Pretraining

B. Reinforcement Learning with Human Feedback (RLHF)

C. Zero-shot learning

D. Backpropagation

21 / 25

Question 21. Which of the following is true about ChatGPT's training data?

A. Only Wikipedia articles

B. A mixture of web data, books, and coding resources

C. Only OpenAI internal files

D. Tweets and social media only

22 / 25

Question 22. What is a significant risk of relying entirely on generative AI for research?

A. Too much accuracy

B. It's too fast

C. It may fabricate or "hallucinate" facts

D. It can't summarize information

23 / 25

Question 23. Which open-source model is considered an alternative to ChatGPT?

A. GPT-2

B. LLaMA

C. Gemini

D. AlphaGo

24 / 25

Question 24. In transformer architecture, what enables parallel processing of sequences?

A. Feedback loop

B. Positional encoding & multi-head attention

C. Gradient clipping

D. ReLU activation

25 / 25

Question 25. What is the output of a language model during inference?

A. Compressed tokens

B. Generated text or response

C. Confusion matrix

D. Vector embeddings only

Your score is

The average score is 2%

References

OpenAI – Research & Blog
https://openai.com/research
OpenAI – GPT-4 Technical Report (2023)
https://openai.com/gpt-4
Vaswani et al. (2017) – “Attention Is All You Need”
https://arxiv.org/abs/1706.03762
DeepLearning.AI – Generative AI with Large Language Models (Coursera)
https://www.coursera.org/learn/generative-ai-with-llms
Hugging Face – Documentation & Tutorials
https://huggingface.co/learn
Anthropic – Claude AI Overview
https://www.anthropic.com/index/claude

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ChatGPT and Generative AI – How It Works

Introduction

What you’ll learn

References

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