What is an LLM?

LLM (Large Language Model) is an artificial intelligence model trained on large volumes of text and other types of data. These models can analyze human language, generate text, answer questions, summarize information, translate content, and perform a wide range of language-based tasks. Large language models power AI systems such as ChatGPT, Gemini, and Claude. When a user sends a question or instruction to the system, the LLM analyzes the input and generates an appropriate response based on the language patterns it has learned. LLMs are one of the core technologies behind modern Generative AI and are now used in many areas, from chatbots and programming assistants to document analysis and intelligent search systems.

How does an LLM work?

Large language models work by learning patterns in language and the relationships between words. During the training process, an LLM is exposed to large amounts of textual data. This data may include books, articles, web pages, programming code, and other text-based sources. During training, the model learns which words and expressions are likely to appear together in different contexts. When a user later sends a prompt, the system analyzes the context and predicts the most likely next word or piece of text. This process is repeated many times within seconds, gradually forming a complete response.

What is a Token?

An LLM does not process text directly as complete sentences. Instead, the text is first divided into smaller units called tokens. A token may represent a full word, part of a word, a punctuation mark, or another text element. For example, a long word may be split into several different tokens by the model. A user's prompt is first broken down into tokens, after which the model analyzes the relationships between them and generates the response token by token. For this reason, the concept of “token count” is important in LLM systems both in terms of how much text a model can process and the computational cost of using the system.

What is a Transformer and how is it related to LLMs?

Most modern large language models are built on a neural network architecture called the Transformer. The Transformer enables the model to evaluate relationships between different words and expressions within a text. One of its main mechanisms is attention, particularly self-attention. Self-attention helps the model identify which parts of a sentence or larger text are most relevant to one another. This allows the model to focus not only on individual words but also on the context in which they appear.

What can an LLM do?

Large language models can be used for a wide range of tasks. They can generate text, answer questions, summarize long documents, translate content into different languages, write programming code, explain existing code, and search for information within text. LLMs are also used to create intelligent chatbots, virtual assistants, document analysis systems, and AI solutions that work with internal company knowledge bases.

Are LLM and Generative AI the same thing?

Although LLMs and Generative AI are closely related, they are not the same concept. Generative AI is a broader category of artificial intelligence systems that can create new content. This content may include text, images, video, audio, programming code, and other formats. An LLM, on the other hand, is a large artificial intelligence model mainly designed to work with language and text. In other words, LLMs are an important part of Generative AI technologies, but Generative AI is not limited to LLMs.

What is the relationship between LLMs and Machine Learning?

Machine Learning is a field of artificial intelligence that enables computer systems to learn patterns from data. Deep Learning is a Machine Learning approach that uses multi-layered neural networks. LLMs are also based on Deep Learning technologies. Therefore, large language models can be understood within the following hierarchy:

Artificial Intelligence → Machine Learning → Deep Learning → Large Language Model

Where are LLMs used?

LLM technology is already being applied across many industries. Examples include developing chatbots that answer customer questions, finding information within large documents, automatically analyzing and classifying text, generating and explaining code, and accelerating the creation of emails and reports. LLMs are also used to develop different AI-powered solutions in areas such as education, finance, e-commerce, media, software development, and many other industries.

What are the limitations of LLMs?

Although large language models are highly capable, their responses should not automatically be assumed to be correct. An LLM may sometimes present factually incorrect information in a convincing way. This phenomenon is known in the AI field as a “hallucination.” A model’s responses also depend on the data it was trained on, the context provided by the user, and the quality of the prompt. Therefore, especially in sensitive areas such as law, finance, healthcare, and other high-stakes fields, information generated by an LLM should be verified using additional reliable sources.

What is the difference between LLM and RAG?

An LLM generates responses based on the knowledge it acquired during training and the context provided to it. However, a model may not always have access to a company’s internal documents or the most recent information. RAG — Retrieval-Augmented Generation — is an approach used to reduce this limitation. In a RAG system, relevant information is first retrieved from an external data source. That information is then provided to the LLM, allowing the model to generate its response using the additional context. This approach is especially useful for document-based question-answering systems, corporate chatbots, and internal knowledge bases.

What should you learn to work with LLMs?

For those who want to build LLM-based systems, foundational knowledge of programming and artificial intelligence is important. Python, Machine Learning, Deep Learning, NLP, APIs, data processing, Transformer architecture, Prompt Engineering, and RAG systems are among the key topics to learn in this field. Working with LLMs today is no longer limited to simply using ready-made chatbots. Artificial Intelligence Engineers integrate large language models into different applications, build systems that work with specialized data, evaluate model outputs, and adapt AI products for real-world use. If you would like to learn about LLMs, Generative AI, Machine Learning, RAG, and other modern artificial intelligence technologies and develop your skills in this field, you can explore the Artificial Intelligence Engineering course offered by JET Academy.

If you would also like to gain an in-depth understanding of LLMs and other artificial intelligence technologies, join our AI Engineering course at JET Academy!