How a Chat Like ChatGPT Works

A chat like ChatGPT is based on artificial intelligence specialized in understanding and generating human language.

It is not a program that provides predefined answers, but a system that builds each reply in real time, starting from what the user writes.

Training on Human Language Through Vast Amounts of Texts

To generate credible and relevant responses, artificial intelligence undergoes a lengthy training process based on analyzing an enormous amount of text. These texts come from highly diversified and reputable sources: public books, academic articles, reference websites, discussion forums, technical manuals, instructional documents, and many public online conversations. Some of the most used sites in the training process include Wikipedia, StackOverflow, GitHub, arXiv, online newspapers, industry blogs, open-access academic journals, and government portals. Every text contributes to giving the model a comprehensive view of styles, lexicons, topics, and expressive modalities.

Learning takes place through a technique called “supervised machine learning,” which enables the system to recognize relationships between words and predict the most likely sequence of terms in a given context. In practice, the model analyzes billions of sentences and learns to sense which word, concept, or grammatical construction is best suited as a reply to a specific input. This phase is entirely automatic but guided by strict quality and safety criteria, which filter out harmful content, fake news, and unsuitable material.

A recent and significant development concerns the collaboration between OpenAI and Harvard Library, which has decided to open part of its immense digital book collection for ChatGPT’s training processes. Thanks to this initiative, for the first time, artificial intelligence can draw directly from high-level scientific books, historical texts, and fundamental works of world literature preserved at Harvard. This marks a major step forward, as it allows the model to answer even complex topics and provide references directly from authoritative texts previously inaccessible to AI. Including these new sources will greatly increase the depth and accuracy of the knowledge offered in responses, bringing the system ever closer to a level of broad and up-to-date general culture.

Understanding the User’s Message

When a person writes something in the chat, the system tries to interpret the meaning of that sentence. It does not simply translate word for word but analyzes the context, nuances, tone, and intention of the message. If you write a question, it understands that you expect an answer; if you give a command, it understands that you want it to perform a linguistic action. Its priority is to formulate a response that is consistent with what it perceives as your communicative intent.

Response Generation Based on Predicting the Next Word

Once the message is understood, the AI begins to generate the answer, one word at a time. It does not pull from an archive of pre-written responses but decides step by step which word should logically come next. The model calculates, among all possible words, which is the most likely to use in that specific context, and then continues this way for the entire sentence, until the whole reply is constructed. This method is based on a technology called an “autoregressive language model,” which essentially works by predicting the next word in a sequence.

Limits and the Possibility of Real-Time Web Searches

Although this artificial intelligence can generate highly convincing answers, it still has some important limitations. The model, by default, has no awareness, no emotions, and no direct access to everything that has happened in the world after the date it was trained, so some information may be outdated or incomplete. However, some modern versions and platforms of ChatGPT are equipped with the ability to perform real-time web searches. In these cases, when it receives a question that requires up-to-date data, the AI can initiate an online search, consult reliable sources, articles, and public data, and then integrate this information into the response provided to the user. This function overcomes the limitations of static training and offers answers based on news, statistics, or recent events, always requiring a critical evaluation of the quality and reliability of the sources used. The ability to connect to the web significantly expands the AI’s field of use but does not completely eliminate the risk of errors, since the quality of the answers also depends on what is available on the internet at that moment.

Answering User Requests About Uploaded Files

When a user uploads a file, such as a PDF document, spreadsheet, or text document, and asks a question about that file, ChatGPT uses a process that goes far beyond simple surface reading. After having already extracted and reorganized the information from the file, the system searches through all the available data for those most relevant to the user’s request. The answer is constructed using only the content actually present within the file: the AI analyzes sections of text, tables, images, and any other relevant element, identifying those that best respond to the question asked. In this way, it can produce chapter summaries, extract numerical data, explain complex tables, or reformulate parts of text in a simpler way, ensuring that every piece of information provided is truly contained in the uploaded document. This process allows even long and complex documents to be transformed into targeted, detailed, and easy-to-consult answers, avoiding arbitrary interpretations or extrapolations not supported by the original data.

Processing Content and Creating an Internal Map

Once a file is uploaded, the artificial intelligence does not merely store it as a simple collection of pages or images but performs true structured processing. The system extracts every significant element, such as titles, paragraphs, lists, charts, and tables, and arranges them into a kind of “internal map” that organizes all information in an orderly and easily accessible manner. This map allows the AI to quickly navigate the document, identify key points, recognize links between different sections, and trace the origin of each piece of information. In practice, it creates a digital representation of the file that facilitates any kind of operation requested by the user, such as summarizing a specific section, comparing data between tables, or explaining the results of an analysis found in the text. The entire process occurs automatically and transparently, without the user having to worry about special formatting or manually specifying areas of interest.

Ability to Summarize and Simplify Complex Texts

One of ChatGPT’s most important skills is its ability to summarize very long and complex texts into shorter, easily understandable versions. When given a complex text, such as a scientific article, technical manual, or a long email, the system can identify the main concepts, select key information, and leave out secondary or repetitive details. This allows the user to get a clear overview of the topic without reading the whole document. Moreover, the model can adapt the level of detail in the summary according to the request, producing either brief abstracts or detailed explanations, and simplifying the language as needed, making even difficult or highly specialized content accessible.

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