This content has been automatically translated from Ukrainian.
A few years ago, conversations with artificial intelligence were associated by most people either with science fiction or with rather primitive chatbots that could only respond to predefined questions. The emergence of ChatGPT at the end of 2022 sharply changed this perception.
It turned out that one could communicate with a computer system almost in ordinary language: ask it to explain a complex topic in simple terms, find an error in code, come up with a title, shorten a long document, create a travel plan, or continue a conversation considering previous messages.
Since then, ChatGPT has changed significantly. Today, it is no longer just a field where questions are entered and text responses are received. Depending on the available features, it can work with images and files, search for information on the internet, analyze data, communicate verbally, conduct complex research, and use other tools.
But to understand what exactly happens "under the hood," it is first worth understanding the broader concept of artificial intelligence.
What is artificial intelligence
Artificial intelligence, or AI, is a general term for technologies that allow computer systems to perform tasks that typically require abilities associated with human intelligence.
For example, recognizing speech and objects in photographs, finding patterns in large data sets, translating texts, making predictions, or generating new content.
Artificial intelligence does not necessarily look like a conversational partner in a chat. AI algorithms have long been working in YouTube and Netflix recommendations, spam filters, facial recognition systems, navigators, automatic translation, and many other services.
ChatGPT belongs to a separate direction — generative artificial intelligence. This refers to systems capable of creating new content: text, images, program code, audio, and other materials.
What is ChatGPT in simple terms
ChatGPT is a service created by OpenAI based on artificial intelligence models that can be interacted with using ordinary language.
The user writes a request — often called a prompt — and the system generates a response, taking into account the request itself, the context of the conversation, and the tools available to it.
The name ChatGPT consists of two parts.
Chat indicates the dialog format of interaction.
GPT is short for Generative Pre-trained Transformer.
This can be roughly translated as "generative pre-trained transformer."
Generative means that the model can generate new content. Pre-trained means that before use, it undergoes extensive pre-training. And Transformer is the name of the architecture of neural networks on which the GPT family is based.
It is important not to confuse GPT and ChatGPT. GPT is a family of artificial intelligence models. ChatGPT is a product in which such models are used together with an interface and various additional capabilities.
Similarly, OpenAI is the company that develops ChatGPT and GPT models.
How ChatGPT generates responses
In a very simplified form, the functioning of the language model can be explained like this: it has learned to determine which fragment of text is most likely to appear next, considering everything that has been written before it.
However, instead of whole words, modern language models primarily work with smaller units — tokens. A token can be a word, part of a word, punctuation mark, or another fragment of text.
During pre-training, the model analyzes vast amounts of information and learns to find patterns within it: how sentences are constructed, which concepts are related to each other, what program code looks like, what the structure of an article is, how people formulate questions and answers.
This does not mean that inside ChatGPT lies a vast encyclopedia from which it simply copies the required paragraph. During training, the model forms a complex system of numerical parameters in which the identified patterns and connections are encoded.
The principle of predicting the next token may sound too simple for a system capable of writing code or explaining quantum physics. But when the model is trained on very large amounts of data and has a vast number of parameters, much more complex capabilities arise from this mechanism.
Scaling language models has become one of the key directions in the development of the GPT family. In 2020, OpenAI introduced GPT-3 — a model with 175 billion parameters that could already perform many language tasks based solely on a text instruction or a few examples.
Why ChatGPT does not just continue text
One round of pre-training is not enough to obtain a convenient digital assistant.
A model trained only to predict the next fragment of text does not necessarily understand correctly that a person wants an answer to a question, not just a continuation of what has been written.
This is where another stage of OpenAI's development became important — training models to follow human instructions.
In early 2022, the company introduced InstructGPT. To create it, among other things, reinforcement learning based on feedback from humans was used — RLHF, Reinforcement Learning from Human Feedback. People evaluated different responses from the model, and the system learned to respond better to human expectations.
This approach became one of the important steps towards the emergence of ChatGPT.
From the first GPT to ChatGPT
The history of ChatGPT actually began long before the launch of the service itself.
OpenAI was founded in 2015, and by 2018, the company's researchers published a paper on a model built on the principle of generative pre-training. This model later became known as the first GPT.
In 2019, GPT-2 appeared. It could generate texts that were so coherent for its time that OpenAI initially decided not to release the largest version of the model due to concerns about potential misuse. The full model with 1.5 billion parameters was released later that same year.
The next major step was GPT-3 in 2020. The scale of the model increased sharply, along with the number of tasks it could perform without special retraining for each individual task.
In 2022, OpenAI introduced InstructGPT — models better suited for following human instructions. And the experience with such systems directly led to the creation of ChatGPT.
When did ChatGPT appear
ChatGPT was officially launched on November 30, 2022.
Initially, it was a research release through which OpenAI wanted to gather more information from users about the strengths and weaknesses of the system. The first ChatGPT was fine-tuned on a model from the GPT-3.5 family and used RLHF methods.
The main feature was not just the level of the language model itself, but the form of interaction.
Instead of having to come up with a separate complex request each time, the user could engage in a normal dialogue:
"Explain this to me more simply."
"Give an example."
"And now compare it with the previous version."
"Rewrite it, but shorter."
ChatGPT took into account previous messages within the conversation, making interaction with a large language model much more accessible for people without technical knowledge.
In its first announcement, OpenAI specifically mentioned the dialog format as one of the main features of the product: it allowed for clarifying questions and continuing previous conversations.
GPT-4 and the transition from chatbot to universal assistant
The next major milestone came on March 14, 2023, when OpenAI introduced GPT-4.
The model became stronger in complex tasks and, unlike earlier text-based GPTs, was multimodal: it could work not only with text but also with images as input data.
Throughout 2023, ChatGPT itself also gradually gained new features. The ability to work with files, analyze data, use additional tools, and recognize images gradually changed the very nature of the product.
ChatGPT began to resemble less a traditional chatbot and more a universal interface for interacting with artificial intelligence.
GPT-4o: text, images, and voice
In May 2024, OpenAI introduced GPT-4o. The letter o stands for omni — "all-encompassing."
The model was designed to work simultaneously with different types of information: text, images, and audio. This made voice interaction with ChatGPT much more natural, and working with images became an important part of the product itself.
From now on, a typical scenario could look nothing like text messaging. The user could show the system a photo, ask it to explain what is in it, ask an additional question verbally, and continue the conversation.
ChatGPT accesses up-to-date information
Early language models had an important limitation: their knowledge depended on the information used during training. If an event occurred after this, the model might simply not know about it.
Thus, ChatGPT and a search engine were initially fundamentally different things.
The situation began to change with the emergence of web tools, and on October 31, 2024, OpenAI separately launched ChatGPT Search — a web search with the ability to use current web sources and provide links to them. By early 2025, this feature became available to all users in regions where ChatGPT operates.
This important distinction remains today: the response of a language model and the response after an internet search are not quite the same.
If the system uses web search, it can obtain fresh information from external sources. Without it, the response is primarily based on the knowledge and patterns learned by the model during training, as well as the context provided by the user.
From responses to research and task execution
In 2025, the development of ChatGPT went even further.
In February, OpenAI introduced Deep Research — "deep research". This feature is designed not for quick answers to single questions, but for multi-step searches: the system can process various sources, correlate information, and prepare structured material with references.
In July 2025, ChatGPT agent was introduced. Agent capabilities mean that the system can not only explain how to perform a certain digital task but also, within the limits allowed by the user, work with tools and complete individual stages of the task independently.
Thus, ChatGPT gradually evolved from an experimental text chatbot to a platform that combines language models, search, document and image handling, data analysis, research, and agent tools.
The development in this direction continued in 2026. In July, OpenAI began implementing GPT-5.6 Sol into ChatGPT — a new flagship model for complex tasks, including programming, research, science, computer work, and design. That same month, the company introduced ChatGPT Work — an agent mode focused not only on responses but on long-term work with files, applications, and complex projects. These changes further solidified the transition of ChatGPT from a regular dialog service to an environment for executing complex digital tasks.
What ChatGPT is used for
The simplest way to understand the capabilities of ChatGPT is to perceive it not as "a machine that knows the answers to all questions," but as a universal tool for working with information and content.
It is used for learning and explaining complex topics, brainstorming, preparing and editing texts, translation, programming, document and spreadsheet analysis, interview preparation, information structuring, research, planning, and creative tasks.
For example, a student might ask for a mathematical principle to be explained with a simple example. A programmer might ask to find an error in code. An entrepreneur might want to structure market research results. An author might check the logic of an article or come up with several title options.
An important advantage of the dialog format is that the result can be clarified. It is not necessary to compose a perfect request right away: one can ask to change the tone, explain a certain point in more detail, suggest an alternative, or take a new condition into account.
Does ChatGPT "think"
Everyday phrases like "ChatGPT thinks" or "AI decided" are convenient but can create a misleading impression of the technology.
ChatGPT is not a person behind a computer and does not have its own biography, life experience, or personal beliefs. Responses arise as a result of the operation of a mathematical model and software systems, not because there is a digital personality inside the service that "remembered" something.
At the same time, reducing modern models simply to "autocorrect on a phone" is also inaccurate. The scale of training and architecture allow them to perform complex multi-step tasks, work with abstract concepts, analyze information, and transfer learned patterns to new situations.
That is why debates about what should be called "understanding" or "reasoning" in the context of AI largely depend on how we define these words themselves.
Why ChatGPT can make mistakes
One of the most important things to know about ChatGPT is that a convincing answer is not always a correct answer.
The language model generates text that statistically and semantically corresponds to the request. However, the confidence of the formulation is not proof of its truthfulness.
The system may confuse a date, misinterpret a question, make a calculation error, or even create a plausible fact or source that does not actually exist. Such mistakes are often referred to as AI hallucinations.
OpenAI pointed out this problem as early as the first description of ChatGPT in 2022, noting that the model sometimes generates responses that sound convincing but are incorrect or nonsensical.
That is why ChatGPT is particularly useful not as an infallible "electronic oracle," but as a tool whose results can be verified, clarified, and supplemented with other sources.
This is especially important for medicine, law, finance, science, and other fields where a mistake can have serious consequences.
Is ChatGPT a search engine?
Not quite.
A search engine traditionally finds pages that already exist on the internet and offers the user to navigate to them. In contrast, the language model forms the response itself according to the request.
The modern ChatGPT can combine both approaches. For one task, the model's capabilities are sufficient; for another, it can resort to web search, find current sources, process them, and form a summarized response.
Therefore, the line between a chatbot, search engine, editor, analytical tool, and digital assistant is gradually becoming less clear.
How ChatGPT differs from a regular chatbot
A classic chatbot often operates on a fairly simple scheme: if the user writes a certain phrase, the system finds the corresponding pre-prepared script.
For example, a bank bot may know that for the request "how to block a card?" it needs to show a specific instruction.
ChatGPT does not require a separately scripted scenario for each possible formulation. The large language model allows it to work with new requests that developers have never literally asked it.
This versatility has become one of the reasons why ChatGPT was perceived not just as another chatbot, but as a new way of interacting with a computer.
What ChatGPT has changed
The main innovation of ChatGPT was not the invention of artificial intelligence or even large language models — they existed before.
Its role was different: a complex technology received an extraordinarily simple interface — ordinary conversation.
To use a powerful language model, it was no longer necessary to be a programmer, work through an API, or understand the principles of machine learning. It was enough to write what you needed.
That is why the word "chat" in the name today somewhat diminishes the capabilities of the product. ChatGPT started as a conversational interface to a language model but gradually transformed into an environment where AI can work with text, voice, images, files, web sources, and other digital tools.
The history of ChatGPT also clearly shows how quickly modern artificial intelligence is changing: between the launch of the first public version in November 2022 and the emergence of search, deep research, and agent functions, only a few years passed.
How to use ChatGPT correctly
To work with ChatGPT, it is not necessary to know a special command language. A request can be formulated just like a request to another person: explain what exactly needs to be done, provide the necessary context, and, if important, specify the desired format of the result.
For example, instead of a short "write a text about solar panels," one can clarify: "Write an introduction to an informative article about home solar panels for a reader who has not encountered them before. Without complex technical terms, approximately 1000 characters."
In general, the more substantial information the model receives, the easier it is for it to understand the task. In OpenAI's official guidelines for crafting prompts, it is advised to be specific, provide sufficient context, and refine the request during the conversation.
At the same time, there is no need to create a huge "perfect prompt" for every question. One of the advantages of ChatGPT lies in the dialog format. If the first response is not suitable, one can write: "explain more simply," "give more examples," "check the facts," "do not use professional jargon," or "keep the structure but shorten the text by half."
It is also useful to inform ChatGPT about the limitations of the task. For example, the budget for a trip, the reader's level of knowledge, the version of the program, the maximum volume of text, or the products available at home. For many practical tasks, context influences the result more than any special "magic" formulations.
And when the answer depends on current data — news, prices, legislation, schedules, or characteristics of modern products — it is worth directly asking to check the information online and provide sources if such a possibility is available.
What ChatGPT knows about the user and how memory works
During a regular conversation, ChatGPT takes into account the messages that have already been written in that chat. That is why after a long explanation, it is not necessary to repeat the topic from the very beginning each time.
Separately, ChatGPT has a memory feature that allows it to use useful context in future conversations. For example, the system can consider what language the user usually communicates in, what project they are working on, or what format of responses they prefer.
In 2026, OpenAI began implementing an updated memory system. When it is turned on, ChatGPT can automatically identify useful context from previous chats, files, and connected applications and form a memory summary that is updated over time. The user can view this summary, correct or delete specific information, and completely turn off memory. The specific appearance of the feature may vary depending on the plan and region.
It is important to note that memory and regular chat history are not the same. Deleting a specific chat does not always automatically remove all the information that ChatGPT may have remembered based on it. To completely delete certain information, it may be necessary to remove it from both memory and the sources where it is contained.
For conversations where the context does not need to be used further, there is a Temporary Chat. It does not use existing memory and does not create new memory, is not stored in the regular history, and is not used for training models. OpenAI notes that such conversations are deleted from its systems within 30 days.
Privacy and security: what to know before using
ChatGPT often works with information provided by the user: texts, documents, photos, spreadsheets, or other files. Therefore, before sending data, it is worth asking oneself the same question one would ask before uploading them to any other online service: do they contain information that should not be transmitted to a third-party system?
Particular caution should be exercised regarding passwords, access codes, payment data, confidential documents, trade secrets, and personal data of other people.
ChatGPT has a separate section called Data Controls, through which one can manage some of the data usage settings. For personal accounts, the user can, in particular, turn off the Improve the model for everyone option. After this, new conversations will not be used for training OpenAI models. For ChatGPT Business, Enterprise, and Edu, as well as the OpenAI API, by default, the provided input and output data are not used for training models.
This is another reason to distinguish between chat history, memory, and data usage for model improvement. These are different mechanisms with separate settings. For example, one can keep chat history but refuse to use new chats for training models.
At the same time, no settings override the basic rule of digital hygiene: if the information is so confidential that its leak could cause serious harm, it is better not to transmit it to an online service without a clear understanding of the rules for data storage and processing.
Will ChatGPT replace Google, programmers, authors, and other professions?
The emergence of ChatGPT has spawned two opposing extremes. According to one version, artificial intelligence will soon replace most specialists. According to another, it is just a slightly more advanced chatbot that will fundamentally change nothing. The real picture is more complex.
ChatGPT is already taking on certain tasks that were previously fully performed by humans: it can create a draft of a letter, translate text, summarize a document, write a code fragment, propose a formula for a spreadsheet, or find information. However, a profession usually consists of many different tasks, not just one operation.
This difference is well illustrated in studies of the impact of generative AI on employment. In the International Labour Organization report for 2025, it is noted that approximately one in four workers worldwide is employed in a profession that is to some extent affected by generative AI. At the same time, researchers consider the transformation of work to be a significantly more likely scenario than the complete automation of most professions, as many tasks still require human involvement.
For example, ChatGPT can help a programmer write a function or find an error, but creating a software product also requires understanding requirements, architecture, testing, security, and accountability for the final result. An author can receive a draft text but still determines the topic, checks facts, decides what to keep, and is responsible for the published material. A similar situation arises in marketing, analytics, education, design, and many other fields.
Data on the actual use of ChatGPT also show that people often use it as an assistant. A large study by OpenAI and Harvard University economist David Deming, published in 2025, showed that a significant portion of interactions are related to information searching, practical advice, and writing tasks. In a work context, creating and editing texts is particularly common.
By 2026, usage is also shifting from simple querying to executing specific tasks: according to OpenAI, in work conversations, users are more than twice as likely to ask ChatGPT to do or create something than in personal chats.
The situation with Google is similar. ChatGPT can already perform some functions for which a search engine was previously opened: quickly explain a concept, compare options, or find the necessary information. However, search engines remain a way to directly find websites and primary sources, while ChatGPT can process the found materials itself and form a response based on them.
Therefore, the question "what will ChatGPT replace?" is gradually giving way to another: what tasks will people perform differently thanks to AI?
The telephone did not make communication unnecessary, the calculator did not destroy mathematics, and search engines did not eliminate the need to know something. They changed how people perform certain actions. With ChatGPT, a similar process is likely occurring — only this time it affects a very wide range of intellectual work.
