How Does ChatGPT Work?

ChatGPT is one of the most popular software products on the planet. It became the fastest app in history to reach 100 million users, taking just two months (compared to the second-fastest, TikTok, which took nine months). Although other AI platforms like Gemini, Perplexity, Claude, and DeepSeek haven’t garnered the same level of popularity, AI assistants and chatbots are here to stay. Let’s take a closer look at the industry leader, how it works, what its limitations are, and how it differs from other available tools.

What is ChatGPT?

ChatGPT is a generative LLM (large language model) from OpenAI. This means that it’s designed to generate human-like text responses based on input prompts from users, whether explaining a concept, solving a problem, or creating a fictional story. This has made it an incredibly valuable tool for businesses, creative endeavors, and personal use, leading to its explosive adoption and growth.

We’re not going to cover the general concept of what AI is in this article, but if you’d like to take a step back before diving into the specifics of ChatGPT, check out the link below:

Learn more about how generative AI and other types of AI work here.

How Many Parameters Does ChatGPT Have?

Parameters in AI are internal settings that guide how the model processes and generates text (or other media like images or audio). Think of ChatGPT’s parameters as the rules that define its decision-making and “knowledge.” Because of this, every model within ChatGPT has different parameters to optimize it for certain use cases. Some of the current and popular models with their estimated parameter counts can be found in this table:

How Does ChatGPT Learn & How Was it Trained?

All of ChatGPT’s learning happens during the two main stages of training: Pre-training and fine-tuning. Nothing from individual conversations affects the model directly. However, OpenAI does use anonymized and aggregated usage data to improve future models.

Pre-Training

The first phase of training an LLM like ChatGPT involves processing massive amounts of information from things like books and websites This is because current LLMs require huge amounts of data and text (think hundreds of billions of documents) to “learn” language patterns, grammar, how to reason, and general knowledge and facts. Although this is supposed to be restricted to publicly available data, some AI companies have come under fire for using copyrighted works or information without the creator’s approval.

From here, ChatGPT was repeatedly tested to predict the likeliest next word in sentences, developing a knack for creating text that sounds human-written (even if the AI doesn’t “understand” what it’s saying). This stage of training also results in the initial parameter values for the model.

Fine-Tuning

From there, OpenAI fine-tuned each model with more targeted datasets based on its focus — this might be audio of conversations and people talking, or massive amounts of coding documents. This stage of ChatGPT’s training also uses reinforcement learning, where human testers score responses. This rating system helps the LLM further tune its generative outputs to align with human preferences and guidelines for safety. For those interested, we dig deeper into the different methods of AI training in our article about types of AI.

How Does ChatGPT Work to Generate Responses?

ChatGPT generates responses by predicting the next word in a sentence — it doesn’t understand concepts or answer based on its own knowledge. But how does it retrieve this information to answer any question or prompt you throw at it?

In Simple Terms

The first thing ChatGPT does is process the input text to clean up typos, missing punctuation, and other factors to form a clear prompt it can understand. From there, it begins generating a response one token (word, punctuation mark, or other grammatical piece) at a time. This involves evaluating the most likely word to follow the previous, based on its training data and parameters.

For example, for the partial phrase “How does ChatGPT…”, the probability calculation might score the next word “work” (“How does ChatGPT work”) the highest, followed by “operate,” “learn,” and so on. Think of it like autocomplete, except with no user input once started. This is repeated over and over for each token until the response is complete.

It’s worth reiterating that ChatGPT doesn’t think in ideas like humans — it calculates how likely the next word is to be a certain term. However, its massive training dataset has gotten ChatGPT responses to very closely imitate human responses, making it easy to believe that it’s “thinking.”

In Technical Terms

For a more detailed breakdown, let’s circle back to the input prompt. While we simplified this step in the previous section, it’s worth understanding how it processes text. ChatGPT tokenizes the input, similar to output tokens, by breaking the text into smaller units like words or characters. Using a multi-layered neural network (also explained in our Types of AI article), it weighs the importance of each token and encodes context to the prompt so the LLM can better understand the input. After this, the tokens are passed through a feedforward neural network to apply grammatical rules, semantic meaning, and world knowledge. These final layers help solidify a deeper understanding of the prompt, setting the model up to respond. Again, note that when we say “understanding,” we really mean a high-dimensional vector representation of patterns the model has seen before.

To begin generating, ChatGPT simply predicts the first word based on probability — what word is most likely to start a response, based on similar prompts and responses in its training data. These are often words like “the” or “this,” but vary from prompt to prompt. Once the first token is in place, it repeats this probability calculation again and again, re-analyzing the entire string up to that point to identify the next word.

For example, to answer a question about the best project management software, the first token might be “the.” From there, it might predict that the next word should be “top,” followed by “project,” “management,” and “tool” to start a response. At each step, ChatGPT isn’t predicting the continuation – it’s using the context of everything so far to predict the next word. Then, it does that over and over until its response is complete.

When it comes to larger responses, such as multiple paragraphs, ChatGPT’s training data informs it on how explanations are typically structured. The LLM knows how many words sentences generally contain, what phrases help transition between ideas, when to incorporate bulleted lists, examples, or definitions, and more. This all comes from the tokenization process mentioned at the start of this section, breaking down millions of documents in training data into tokens, analyzing their vectors to associate clusters of words and parameter rules with structures to craft human-like responses.

ChatGPT’s Limitations

As with most generative LLMs, ChatGPT has inherent limitations in its capabilities and uses. Because it relies on next-token prediction, it may bring up incorrect information or hallucinate (especially with pre-existing data like statistics). As we’ve discussed, it also doesn’t comprehend what its responses mean, they’re generated based on patterns. Because of this, these LLMs are not ideal for especially complex problem-solving or dilemmas. However, the recent releases of reasoning models like o1 and o3 are a step towards covering this gap in many use cases.

ChatGPT’s current memory is limited, sometimes resulting in it forgetting instructions or context in older parts of conversations. Its lack of real-time learning also means it can’t truly adapt to more individualized needs or preferences. As with all current AI models, training bias is a major concern that can hamper ChatGPT’s effectiveness and accuracy. Training datasets are so massive that most have to be automated — but if not properly cleaned and normalized, small data biases can become amplified in responses over time.

How Does ChatGPT Write Code?

So far, we’ve primarily focused on ChatGPT’s written response generation, but it’s also a useful tool for creating and correcting code. While it might seem completely different than conversational writing, the process for generating code is still very similar. Currently, the best ChatGPT models for this are GPT-4 and GPT-4o, which both use the same next-token prediction process as GPT-3.5. The key distinction is that these prompts trigger the model to lean more on coding-specific training data from the fine-tuning stage.

Current GPT models were likely trained on billions of lines of code in various languages to form an understanding of syntax, commands, libraries, frameworks, and more. With this training, a model breaks problems into logical steps first, then leverages coding best practices and patterns it has seen before to solve each step. ChatGPT is best suited to Python, JavaScript, and SQL. Because this relies on the next-token method for generation, ChatGPT may still hallucinate things or generate inefficient or insecure code that has to be manually fixed.

How Does ChatGPT Do Math?

Solving mathematical problems is where ChatGPT’s process changes a bit. It relies heavily on pattern recognition and order of operations to process basic problems and equations.  The LLM has seen enough examples to predict the answer “what is 123 + 456” based on patterns alone. However, as the numbers get larger and other complexities are introduced (such as algebra, multi-step logic, etc.), it needs to rely on other methods.

Newer ChatGPT models interface with external tools like the WolframAlpha plugin or Python to compute intricate problems. Essentially, it uses its natural language processing to convert the user’s question into a structure that can be used by WolframAlpha. It then retrieves the final answer from those external tools and returns it to the user.

This is where some of the boundaries of narrow AI become more apparent, as ChatGPT wasn’t built for mathematical computation and instead needs to work in conjunction with other tools.

ChatGPT Alternatives

Some of the most well-known alternatives for ChatGPT are Google Gemini, Perplexity, Anthropic’s Claude, Meta AI, and, more recently, DeepSeek. These are most commonly used as comparisons when it comes to personal use, but based on our research, business users are more focused on other alternatives. Our most common ChatGPT comparisons are with IBM Watsonx, Oracle Digital Assistant, Guru, TensorFlow, and Smith.ai.

Learn more about ChatGPT’s alternatives for businesses here.

How Does ChatGPT Differ From Other AI Chatbots?

So what sets ChatGPT apart from all of these alternatives? Primarily, its versatility. Some platforms like Claude might lean more towards coding, with others like Watsonx and Oracle DA primarily focusing on business workflows. ChatGPT aims to do all this in a single tool, although it can fall short of the more specialized tools in individual fields. While most users agree that ChatGPT isn’t the best AI code generator (GitHub Copilot and Claude 3.7 Sonnet are some of the best), it still stands out and is above average in this area.

Another factor that separates ChatGPT is its integration and plugin ecosystem, working directly with tools like Python, DALL-E, its own search engine, and more. ChatGPT is also particularly strong with creative writing, brainstorming, and brand voice adaptation due to its advanced language proficiency. For businesses, ChatGPT continues to stand out as a flexible option with multiple compelling pricing plans. From individuals to enterprises, it can fit a range of roles at almost any scale.

Find the Best AI Platform for Your Needs

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• Best AI Chatbots
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