ChatGPT Model, Data & Features

What is ChatGPT?

ChatGPT is a Chatbot (a person can chat with a chat software with questions etc) created by US-based artificial intelligence company OpenAI

ChatGPT, most importantly, generates new text/content and handles other stuff, like a human.

GPT stands for “Generative Pre-trained Transformer”. ChatGPT is built on the GPT AI Model where users can chat on top of GPT model technology. Read this if you want to know more about GPT.

1. What type of AI Model was ChatGPT.com selected?
2. How does ChatGPT.com gather the required data?
3. How did the ChatGPT train the data?
4. Open AI Models List
5. What can ChatGPT.com do for you?
6. ChatGPT's secret to a successful product?

What type of AI Model was ChatGPT.com selected?

ChatGPT uses Large Language Models (LLMs) which come under Generative Language Models, built on Transformer architecture.

Large Language Models (LLMs):

1. Large
   1. Very large training datasets
   2. A large number of parameters and tokens
2. Language
   1. All Human languages (There are 5,000+ languages spoken in the world today)
3. Models
   1. Advanced Technology behind AI

LLMs are Pre-trained and Fine-Tuned with Large data sets, Large Parameters, and Large tokens.

Typical LLM and Transformer architecture and components:

There are many components in LLM but the key part is their layers and how the attention mechanism is leveraged. More here

How does ChatGPT.com gather the required data?

ChatGPT 3 leveraged the following data

As you can see,

1. ChatGPT used a lot of free and internet data (unstructured data) from various sources; https://commoncrawl.org/, WebText2, Books data, and Wikipedia data as base data. Note that English Wikipedia has 62+ Million pages
2. ChatGPT also used curated structured data (such as movies, and artists data) sets to improve the quality
3. They had to parse all the unstructured and structured data into tokens and set parameters
4. In AI models, particularly Large Language Models (LLMs), a "token" is the smallest unit of text that the model processes, essentially representing a word, part of a word (sub-word), or even a single character, which is used as the basic building block for understanding and generating language; essentially, it's how text is broken down for the model to analyze and interpret it effectively.
5. Parameters in large language models (LLMs) are internal variables/settings that define how the model processes input and generates output. LLM parameters are variables/settings that control and optimize how the model generates text responses. Those parameter variables/settings include
   1. number of tokens, max_tokens
   2. temperature
   3. top_p (nucleus sampling)
   4. top_k
   5. frequency_penalty
   6. presence_penalty
   7. stop sequences
   8. context window
   9. model size

Summary of ChatGPT.com (version 3 model) data:

1. Parsed 400+ billions of web pages and adding 3 to 5 billion web pages more every month
2. Parsed other 100+ billion pages, books, and Wikipedia
3. Parsed and tokenized the above page data into 500+ billion Tokens
4. Trained with 175 billion parameters autoregressive language model, which they call GPT-3, and measuring its in-context learning abilities. Note that GPT-4 trained with 1+ Trillion parameters.
5. Trained a series of smaller models (ranging from 125 million parameters to 13 billion parameters) as well to compare their performance to GPT-3 in the zero, one, and few-shot setting

How did ChatGPT train the data?

"Training" in generative AI refers to the process of feeding a large dataset of information to an AI model, allowing it to learn patterns and relationships within that data, so it can then generate new content that closely resembles the training data, like text, images, or code, based on the input it receives; essentially, it's the "learning phase" where the model acquires the ability to create new outputs similar to the data it was trained on.

ChatGPT used the above data, parsed, ingested, and trained. It relies on a neural network architecture that is trained on vast amounts of text data to generate responses to user input. The underlying trained data is stored in a distributed files system, to some extent in Azure Cosmos DB, and parameters of the model are stored in memory and updated dynamically during training and inference.

The key part is a Vector store that can store the the large data as vectors, vector index, and vector embeddings after converting the text.

The training requires a ton of computing power. See

To train and run a model with 175 billion parameters needs immense computational resources; powerful GPUs, and extensive memory. A 3-billion parameter model can generate a token in about 6ms on an A100 GPU.

According to one report, just to develop training models and inferencing alone for ChatGPT can require 10,000 Nvidia GPUs and perhaps more.

ChatGPT is estimated to cost OpenAI at around $700,000 per day to run. That shows the required investments to develop AI agents. Therefore, they raised $16+ billion as of Jan 2025.

Open AI Models List

The OpenAI API is powered by a diverse set of models with different capabilities and price points.

They also published open-source models including Point-E, Whisper, Jukebox, and CLIP.

What can ChatGPT.com do for you?

1. Answering Questions

• General Knowledge: I can provide information on a wide variety of topics, including history, science, technology, literature, and much more. I do this by using patterns I’ve learned from tons of data, so I can answer based on that knowledge.
• Factual Clarifications: If you have any questions or need clarification about concepts, I can explain them in detail or offer summaries.

2. Creative Writing

• Storytelling: I can help write short stories, create fiction, or even generate ideas for your own stories.
• Poetry: I can compose poems in various styles, whether it’s haiku, sonnet, or free verse.
• Song Lyrics: If you’re looking for some catchy lyrics, I can generate them in various genres, from pop to rap.

3. Text Generation

• Content Creation: I can assist with writing articles, blog posts, essays, and more.
• Product Descriptions: I can craft compelling descriptions for products, services, or apps.
• Scripts and Dialogues: If you're working on a script for a video, podcast, or even a game, I can generate dialogues and scene descriptions.

4. Language Translation

• Multi-language Support: I can translate text between different languages (e.g., English to Spanish, French to German), and I can also help with basic language learning by explaining grammar or vocabulary.

5. Code Writing and Debugging

• Programming Help: I can write code snippets in various programming languages like Python, JavaScript, Java, C++, and more.
• Bug Fixing: If you’re encountering bugs, I can help troubleshoot and offer solutions.
• Explaining Code: If you need an explanation of what a piece of code does, I can break it down for you.

6. Education and Tutoring

• Math and Science Problems: I can solve mathematical problems (from basic arithmetic to more advanced calculus or algebra) and explain scientific concepts.
• Learning Aid: If you’re studying for exams or learning a new subject, I can help explain concepts, offer quizzes, or even summarize textbooks.
• Language Learning: I can help you learn new languages by practicing conversation, explaining grammar, or providing vocabulary lists.

7. Personalized Recommendations

• Books, Movies, and Music: Based on your interests, I can recommend books, movies, TV shows, or music that align with your tastes.
• Travel Advice: I can offer suggestions for destinations, itineraries, or things to do based on your preferences.

8. Conversational AI

• Chat and Socializing: If you're in the mood for a casual chat, I can have conversations about virtually any topic—whether it's small talk or deeper discussions on philosophy, psychology, or personal experiences.
• Emotional Support: I can provide empathetic responses, listen to concerns, offer advice, or just be a sounding board.

9. Business Assistance

• Brainstorming: I can help generate ideas for business names, product concepts, or marketing strategies.
• Drafting Emails/Letters: If you need to write formal or informal emails, I can help you compose them with the right tone.
• Market Research: I can assist in gathering insights or generating overviews on specific industries or trends.

10. Summarization and Simplification

• Summarizing Long Texts: If you have long articles, research papers, or books, I can summarize them for you, distilling the most important points.
• Simplifying Complex Ideas: If something is too technical or complex, I can break it down into simpler terms so you can understand it better.

11. Problem-solving and Decision-Making

• Critical Thinking: I can help analyze situations, present different viewpoints, and help you weigh the pros and cons when making decisions.
• Advice: If you’re facing a personal challenge or decision, I can provide insights, though I’m not a substitute for professional advice.

12. Games and Fun

• Trivia & Quizzes: I can quiz you on various topics like history, science, or pop culture.
• Puzzles and Riddles: Want to challenge your brain? I can come up with riddles, logic puzzles, or word games.
• Roleplaying: If you want to do a bit of creative roleplaying (e.g., you’re writing a story and need some interaction), I can play different characters.

13. Personal Assistant Tasks

• Task Organization: I can help you organize ideas, make to-do lists, or plan out projects.
• Time Management: I can suggest ways to structure your day, prioritize tasks, or set reminders.

ChatGPT's secret to a successful product?

1. ChatGPT started as open-source research which brought tons of smart people
2. ChatGPT's vision was right with practical improvements
3. They built the right AI model and architecture and they kept on improving models via new model releases.
4. They built very large hardware systems with GPUs because their AI demands many GPUs.
5. They gathered huge & relevant data
6. They trained models continuously with new data and improved the model's accuracy by fine-tuning
7. They invested billions of dollars with initial open-source research supported by many contributors and experiments. A brave and once-in-a-lifetime decision they made to invest, research, and deliver.
8. They built a very simple user interface and handled complexity in the back end

Note: A lot of data and context provided in this post is based on GPT-3 but OpenAI has GPT-4 now.