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Where Does ChatGPT Get Its Data?

The short answer is that ChatGPT is trained on a massive dataset of online text content amounting to hundreds of billions of words. This includes everything from books, articles, forums, websites, and more. The AI scans and analyzes these texts to learn about language, concepts, facts, and how to have natural conversations.

The short answer is that ChatGPT is trained on a massive dataset of online text content amounting to hundreds of billions of words. This includes everything from books, articles, forums, websites, and more. The AI scans and analyzes these texts to learn about language, concepts, facts, and how to have natural conversations.

Where Does ChatGPT Get Its Data?

To truly understand where ChatGPT’s knowledge comes from, we have to dig deeper into how these AI systems are developed in the first place. ChatGPT was created by a research lab called Anthropic using a technique called machine learning.

Here’s a quick overview of how it works:

  1. Gather Massive Text Datasets: Researchers compile vast datasets of online text content to train the AI on. This includes text from books, Wikipedia, news articles, websites, and more.
  2. Train the AI: The AI is shown examples from these texts and learns to generate its own text based on these patterns. It looks for relationships between words, facts, language rules, dialogue patterns, and more.
  3. Improve through Feedback: The AI is given feedback on its responses to improve over time. Humans evaluate its outputs and further refine the model.
  4. Repeat Billions of Times: This training process is repeated billions of times on massive clusters of GPUs and servers to deeply ingrain language understanding into the model.

ChatGPT gets its knowledge from digesting a huge portion of the public internet and books. Its knowledge comes from patterns in how we humans communicate, share ideas, and use language.

What Kinds of Data Does ChatGPT Use?

Specifically, here are some key data sources that were likely used to train ChatGPT:

  • Books – Fiction, non-fiction, textbooks, scientific papers
  • Wikipedia – Encyclopedia articles covering a vast array of topics
  • News articles – Current events, analysis, and commentary
  • Websites – All types of websites for language data
  • Conversations – Dialogue data for conversational ability
  • Technical documentation – Manuals, papers for specialized knowledge
  • Reddit, forums, social media – For informal language and discussions

Researchers have filtered and processed these sources to align with the AI’s goals. But the core training data is sourced from publicly available internet text.

Why Does ChatGPT Get Things Wrong Sometimes?

While ChatGPT’s knowledge is impressive, it’s not perfect. Here are some key reasons why it can sometimes get facts or details wrong:

  • Limited training data – It only has access to a portion of global knowledge.
  • Outdated data – Books and articles used can sometimes be outdated.
  • No fact checking – It doesn’t discern the truthfulness or accuracy of statements.
  • Statistical guesses – It’ll make logical guesses when unsure, which can be incorrect.
  • Limited world knowledge – No actual real-world experiences to draw from.
  • Biased data – Any biases in the original training data get propagated.

Researchers are actively working to improve ChatGPT’s accuracy and rigor through enhanced training techniques and increased feedback. But for now, it’s important to keep in mind its limitations.

How Does ChatGPT Account for Recent or Emerging Topics?

Since most of its training data comes from existing texts, some wonder how ChatGPT handles recent events or new topics. A few ways it can generate relevant responses include:

  • Training on recent news – Some of the dataset likely includes recent news articles.
  • Making logical inferences – It combines its knowledge to make reasoned guesses about new topics.
  • Getting updated – The creators can re-train it on new data to fill knowledge gaps.
  • Asking users – It can defer to the user when unfamiliar with a topic.
  • Offering general principles – Discuss generally applicable principles if specifics are unavailable.

While improvements can still be made, ChatGPT aims to engage helpfully even when new topics come up. Over time, re-training and user feedback will allow it to converse on emerging topics more naturally.

Training ChatGPT for Various Industries

ChatGPT has demonstrated impressive capabilities in conversational AI. However, in its default form, it has general knowledge that may not align well with specialized industry terminology and workflows. Fortunately, ChatGPT’s training process allows for customization for more industry-specific applications.

With proper datasets and techniques, versions of ChatGPT could be tailored for industries like healthcare, finance, technology, retail, and more. Here’s an overview of how ChatGPT could be adapted for various sectors:


In healthcare, a custom ChatGPT model could enable fluid conversations with patients about symptoms, medications, side effects, and more. It could be trained on medical textbooks, research papers, patient conversations, and electronic health record data. This would teach it healthcare vocabulary, diagnostic processes, bedside manner, and empathy.

Such a medical ChatGPT could allow providers to offload routine patient interactions. It could also empower patients with reliable information from a compassionate source. Regulators would need to ensure privacy compliance and validate its responses for accuracy.


For finance, ChatGPT could be trained on earnings reports, financial news, client conversations, regulatory filings, and financial textbooks. This would enable it to discuss financial products, investment strategies, market dynamics, transactions, and more.

Banks could implement conversational AI assistants for customer service interactions. Wealth management firms could use ChatGPT models to engage clients on portfolio performance or investment options. Again, accuracy and regulatory alignment would need to be assured.


Technology companies have vast documentation and conversational data that could train industry-specific ChatGPT models. By ingesting manuals, support transcripts, forum posts, code repositories, and technical papers, the AI could learn to fluently discuss software platforms and troubleshooting.

Such tech-savvy ChatGPT instances could empower developers and assist customers with technical issues conversationally. They could simplify accessing documentation spread across knowledge bases and forums.

Retail & E-Commerce

In retail, ChatGPT could ingest product catalogs, inventory databases, consumer feedback, and transaction records to engage customers in personalized shopping conversations. It could provide product recommendations, inventory availability, order status, and shipping estimates.

E-commerce leaders like Amazon could implement such models to enhance customer experience pre and post-purchase. Brick-and-mortar retailers could deploy customized ChatGPTs at in-store kiosks or integrate them into mobile apps.

The Possibilities Are Vast

These are just a few examples of how targeted training could adapt ChatGPT for specialized industries. The possibilities span every sector from media, to manufacturing, to government. With diligent dataset curation and feedback loops, the capabilities could be immense.

Of course, certain precautions around accuracy, transparency, and regulation will need to be addressed. But with responsible implementation, Industry-specific ChatGPT could automate conversations to drive efficiency and empower both employees and customers. We’re only beginning to glimpse its potential across industries.

Important Questions

Does ChatGPT get data from Google?

No, ChatGPT does not get its training data directly from Google. It was created by Anthropic and trained on a variety of text data sources scraped from across the public internet and books. This includes Google Books, but not Google’s search engine data. The training data does not come directly from Google’s servers. The creators of ChatGPT curated a diverse dataset from various open sources.

Does ChatGPT get its info from the internet?

Yes, a lot of ChatGPT’s training data ultimately comes from internet sources like websites, online books, discussion forums, and other digitally available text. But it’s not connected live to the internet for generating responses. The AI was trained offline on a snapshot of internet data to learn general language patterns and concepts. It does not actively search the web or continue training online. But the internet as a whole provided a wealth of diverse text data to build ChatGPT’s capabilities.

Does chatgpt use a lot of mobile data?

No, using ChatGPT itself does not use much mobile data at all. The chat interface is very lightweight. Behind the scenes, the AI model generating the responses is hosted on Anthropic’s servers. All the computationally intensive work is done in the cloud, not on your device. Any standard data plan should have enough bandwidth for typical ChatGPT use without needing to worry about data costs. The bandwidth is comparable to text-based chatting or browsing simple webpages. Unless frequently downloading huge generated texts, ChatGPT’s impact on mobile data usage is negligible.

Final Words

I hope this gives some clarity on where ChatGPT gets its remarkable breadth of knowledge from. While not perfect, it represents an exciting advancement in AI capabilities driven by open-source, publicly available data. Going forward, transparency about its training methodology will build appropriate trust with users. But we must keep realistic expectations as there are always new frontiers in replicating human knowledge and reasoning.


  • Dave James

    Dave has been gaming since the days of Zaxxon and Lady Bug on the Colecovision, and code books for the Commodore Vic 20 (Death Race 2000!). He built his first gaming PC at the tender age of 16, and finally finished bug-fixing the Cyrix-based system around a year later. When he dropped it out of the window. He first started writing for Official PlayStation Magazine and Xbox World many decades ago, then moved onto PC Format full-time, then PC Gamer, TechRadar, and T3 among others. Now he's back, writing about the nightmarish graphics card market, CPUs with more cores than sense, gaming laptops hotter than the sun, and SSDs more capacious than a Cybertruck.

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