OpenAI's Revolutionary Language Model.

How ChatGPT Actually Works: A Surprising Underlying Mechanism

ChatGPT stood out as a groundbreaking technology a few months ago. People are now fascinated by this revolutionary AI technology. Today we will explain how ChatGPT works, one of the most wondered questions recently.

We have observed that many individuals become frustrated or confused about the provided responses from ChatGPT because it is not what they expect it to be. To clear up this misunderstanding, today we will delve into how ChatGPT works and the technology behind this AI.


ChatGPT, a trained language model, is developed by OpenAI. Since its release, millions of people have engaged in conversation with it. And, the results it has provided were spectacularly good that it has gained exponential popularity around the globe.

However, some individuals have become confused about the answers or received unexpected results. We believe that many of them do not fully comprehend how ChatGPT actually works. If you are interested in learning the underlying technology behind ChatGPT, you have come to the right place.

How does ChatGPT work?

how ChatGPT works

ChatGPT operates on a sophisticated combination of cutting-edge techniques, including deep learning, natural language processing (NLP), and neural networks.

It is trained on vast amounts of text data, enabling it to learn patterns, structures, and semantic nuances in human language. This extensive training allows ChatGPT to generate coherent and contextually appropriate responses, mimicking human-like conversation with astonishing accuracy.

Training the Language Model

At the core of ChatGPT lies a powerful transformer neural network architecture. During the training phase, the model analyzes billions of sentences, developing an understanding of the relationships between words and the context in which they appear. This process, known as language modeling, equips ChatGPT with the ability to predict the likelihood of a word based on its surrounding context, enabling it to generate coherent responses.

What are Large Language Models (LLMs)

Large language models are a type of artificial intelligence. These models are being trained on vast amounts of text data. At the end of this process, they end up being capable of understanding and generating human language. This way, you can use them for a variety of tasks such as language translation, text summarization, and question answering.

There are lots of large language models on the market today. The most famous one is GPT-3 model, developed by OpenAI. They also built upon GPT-3 model. And OpenAI is getting ready to introduce GPT-4 model in 2023. To learn more about this take a look at our post on the most advanced chatbot.

Murray Patrick Shanahan, a professor of Cognitive Robotics at Imperial College London, wrote a paper on this titled “Talking About Large Language Models” on December 2022.

In his paper, he discussed the subject in depth and gave scientific insights into how large language models (LLMs) like ChatGPT work. You can access the paper directly from the internet.

For a concise explanation of how large language models (LLMs) such as ChatGPT works, we are including the excellent following passage from the paper.

Talking About LLMs

LLMs are generative mathematical models of the statistical distribution of tokens in the vast public corpus of human-generated text, where the tokens in question include words, parts of words, or individual characters including punctuation marks.

They are generative because we can sample from them, which means we can ask them questions. But the questions are of the following very specific kind. “Here’s a fragment of text. Tell me how this fragment might go on. According to your model of the statistics of human language, what words are likely to come next?”

It is very important to bear in mind that this is what large language models really do. Suppose we give an LLM the prompt “The first person to walk on the Moon was ”, and suppose it responds with “Neil Armstrong”. What are we really asking here? In an important sense, we are not really asking who was the first person to walk on the Moon.

What we are really asking the model is the following question: Given the statistical distribution of words in the vast public corpus of (English) text, what words are most likely to follow the sequence “The first person to walk on the Moon was ”? A good reply to this question is “Neil Armstrong”.

Talking About Large Language Models by Murray Patrick Shanahan

For those who may be unsure of what this paragraph even means or prefer a simpler explanation, let me clarify: ChatGPT is not a ‘truth-bot’, but rather a language model.

It generates text by determining the most linguistically correct ending to a sentence based on billions of examples from other sources. When you point out its mistakes it might apologize to you and correct itself, but it’s not doing that because it learned something or ‘accepted’ that it’s wrong… It’s doing that because, in the billions of samples it analyzed, apologies usually came after being told that you’re wrong.

So in order to test this, I asked ChatGPT the mentioned question in the passage and received the same result as the following.

how does chatgpt work test

This suggests that when I asked ChatGPT with the prompt “The first person to walk on the Moon was”. It did not possess the answer by truly knowing it. Rather, ChatGPT simply evaluated the question to determine which words were most likely to follow and generated the result based on statistical distributions.

Fine-Tuning for Enhanced Performance

While the initial training provides ChatGPT with a solid foundation, fine-tuning is crucial to enhance its performance in an interactive dialogue. OpenAI employs a technique called reinforcement learning, where human AI trainers act as both users and AI assistants in simulated conversations. This iterative feedback loop allows ChatGPT to learn from targeted examples and improve its responses over time, honing its ability to engage in dynamic and context-aware conversations.

Contextual Understanding and Prompting

To ensure meaningful interactions, ChatGPT leverages contextual understanding and prompting. It takes into account the conversation history, analyzing the preceding messages to provide relevant and coherent responses. By understanding the context, ChatGPT can maintain continuity and relevance throughout the conversation. Additionally, users can provide specific instructions or guidelines through prompts, guiding ChatGPT’s responses and shaping the conversation according to their needs.

Ethical Considerations and Safety Measures

OpenAI places a strong emphasis on ethical considerations and user safety in the development of ChatGPT. Measures are implemented to minimize biased or harmful outputs. The training process encompasses a diverse range of data sources to capture a wide array of perspectives. Furthermore, reinforcement learning from human feedback helps mitigate potential issues and improve the system’s behavior. OpenAI actively encourages user feedback to ensure continuous improvements and address any concerns that may arise.

FAQs About How ChatGPT Actually Works

1. How does ChatGPT handle ambiguous queries or requests?

ChatGPT excels at handling ambiguous queries by utilizing the context provided by the user. By analyzing the conversation history, it can infer the user’s intent and generate appropriate responses. In cases where the context is insufficient or unclear, ChatGPT may ask for clarification to ensure accurate and relevant replies.

2. Can ChatGPT provide detailed explanations or answer complex questions?

While OpenAI designed ChatGPT to provide informative responses, its ability to deliver detailed explanations or answer complex questions may vary. It possesses a broad understanding of various topics, but highly specialized or technical inquiries may pose a challenge. In such cases, ChatGPT aims to provide helpful information within its knowledge base but may not offer comprehensive explanations.

3. Can ChatGPT understand and respond in different languages?

While ChatGPT’s primary operating language is English, it does have some capability to understand and respond in other languages to a certain extent. However, its proficiency and accuracy may vary depending on the language in question. OpenAI continues to explore ways to enhance ChatGPT’s multilingual capabilities to make it more accessible and effective for users worldwide.

4. How does ChatGPT learn and adapt over time?

ChatGPT learns and adapts over time through a process called reinforcement learning. Human AI trainers review and rate model-generated suggestions, providing feedback that helps refine and improve ChatGPT’s responses. This iterative feedback loop enables the model to continually learn and enhance its conversational abilities, becoming more adept at understanding and generating meaningful and contextually relevant responses.


  • Because ChatGPT is so advanced, it can be challenging to understand how it works on your own. Additionally, sometimes, its responses could be incorrect or out of context. Therefore people want to understand the underlying mechanism of ChatGPT.
  • ChatGPT is a large language model. Large language models (LLMs), a type of artificial intelligence, are being trained on vast amounts of data.
  • Murray Patrick Shanahan, a professor at Imperial College London, examined large language models at length in his paper called Talking About Large Language Models.
  • As Shanahan stated in his paper, a typical LLM generates responses based on the statistical distribution of words in a large public database of text, rather than having actual knowledge.
  • So ChatGPT generates text by determining the most linguistically correct ending to a sentence based on billions of examples from other sources.

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5 responses to “How ChatGPT Actually Works: A Surprising Underlying Mechanism”

  1. I keep getting this error. please help

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    Error code: 1020
    Ray ID: 790cfffd3bee8892
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    Timestamp: 2023-01-28 22:00:16 UTC

  2. wow its awesome

  3. Useful read and especially in knowing how it generates an ‘apology’, so it actually brings the world’s response for us. Interesting to see how it responds to scenario based life questions.

  4. 将来が楽しみですね。



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