What Is LLM — Large Language Model?

In this article, we will examine LLMs, the tasks they solve, and what is required for their successful launch.

What Is the Large Language Model

Large Language Models (LLMs) are advanced algorithms trained on massive datasets. They can process text, analyze it, and generate coherent responses that appear as if a human wrote them.

When people talk about LLMs, neural networks, and artificial intelligence, they seem to be three different worlds. But is this really the case?

How Does LLM Differ From Neural Networks and AI?

At the broadest level, we are talking about artificial intelligence, which encompasses all technologies capable of performing tasks that require human intelligence. AI is not a specific technology but a whole family of approaches, including statistical methods, ML, and neural networks.

Neural networks are a narrower concept, referring to models inspired by the principles of the human brain. They are trained on data and used to solve different types of tasks. For example, convolutional neural networks (CNNs) are most often used for image analysis, and recurrent neural networks (RNNs) are used for working with time series.

Large language models (LLMs) are a subtype of neural networks specifically designed to work with textual data. They use a transformer architecture that efficiently processes large volumes of textual information while preserving context even in long text sequences.

Top LLM Use Cases in Today's World

LLMs are used in various fields, helping to automate and improve processes. Let's consider the main areas of LLM usage.

Text and content generation

LLMs are suitable for creating texts of any complexity - from blogs and marketing materials to code. Thanks to their ability to analyze style and content, they generate text that meets specific requirements.

Virtual assistants

Virtual assistants based on LLMs help to solve everyday tasks, such as organizing things. Their main strength is the ability to work with fuzzy and unclear requests.

Note

A fuzzy query is formulated not in the strict form of a command but as an ordinary human statement, where vital details may be missing. For example, "Remind me to do this tomorrow morning" does not contain precise information about what needs to be done, when, and how it relates to other tasks. It will not understand what to do if such a query is passed to a standard algorithm that works with clear commands (for example, "Set an alarm for 7:30 a.m.").

The LLM-based system, on the other hand, is trained on vast amounts of text and can understand the semantic connection of words. It analyzes the query, guesses the user's intentions (for example, "tomorrow morning" is the beginning of the next day, and "do this" refers to the recent context), and performs the necessary action.

Intelligent search

LLM-generated text embeddings expand search capabilities by analyzing query semantics instead of matching keywords.

Multilingual translation

LLM provides high-quality translation, considering the text's context and stylistic features. It outperforms traditional approaches because it can adapt to linguistic nuances, such as idioms or professional terminology.

Summarizing long texts

LLM simplifies information processing by reducing voluminous documents to short summaries that reflect the main ideas.

Chatbots

LLM-based chatbots have become an integral part of customer interaction. They can imitate human communication by responding to complex questions or performing actions, not just talking.

Popular Products Based on Large Language Models

LLM-based products are constantly evolving. New models appear every year that help users solve various problems.

The list of the most popular solutions for 2025 includes:

• ChatGPT by OpenAI is one of the most famous proprietary LLM-based products. The GPT family models, including GPT-4o, GPT-o1, etc power ChatGPT. It is used for text generation, answering questions, programming assistance, training, and many other tasks.
• Google Bard (Gemini). In May 2023, Bard used the PaLM 2 model, and in December 2023, it was transferred to the more powerful generative model Gemini. In February 2024, Bard began to support more than 40 languages due to integration with Gemini Pro, which significantly expanded its capabilities in processing various languages and multimodal data. This model is actively used in business, science, and education for in-depth data analysis and complex reasoning.
• Copilot by Microsoft. Specializes in assisting developers with programming. This model can be integrated into the IDE to generate code snippets, fix bugs, and find optimal solutions.
• Claude by Anthropic. The tool was developed to emphasize the safety of user interaction. This model is often used in the corporate environment to work with large volumes of text, process confidential information, and perform complex text analysis.
• GigaChat by Sber. The Russian product GigaChat supports more than 100 languages and is focused on text and multimodal queries. It differs from other LLM models by its deep adaptation to the Russian language context and culture; therefore, it understands and processes requests in Russian better than others.
• Qwen by Alibaba Cloud. The Qwen model is designed to analyze and generate complex texts. It is actively used in business, research, and educational projects, supports multilingual work, and easily adapts to specific tasks.

Key Concepts When Working with LLMs

At the heart of how large language models work lies several key concepts which are generally common across different models.

Tokens and tokenization

A token is the smallest unit of text (a word, part of a word, or a symbol) that the model works with. Tokenization breaks text into such tokens so the model can analyze them.

Transformers

The basic architecture of LLMs, which use the attention mechanism to process extended contexts and highlight key information effectively.

Embedding

Texts are represented as numerical vectors that reflect their meaning and relationships with other words, which helps models understand the context.

Model bias

Bias in model outputs due to shortcomings or imbalances in the data used to train the model.

Interpretability

The ability to explain how the model arrived at a particular conclusion.

Fine-tuning

Training the pretrained model on specific data to adapt it to solve highly specialized tasks.

Regularization

A technique that reduces the risk of overfitting during the training process. It helps the model better generalize knowledge and work on new data.

Entropy

A measure of the model's uncertainty when making decisions. High entropy indicates more significant uncertainty, meaning the model has difficulty choosing a specific answer or solution. Low entropy, on the other hand, suggests that the model is more confident in its prediction.

Prompt

A text instruction or query with which the user addresses the model. A prompt can contain source data, a specific task, instructions on the style of the answer, or context.

What Is Needed to Train Your LLM

Creating and maintaining large language models is a resource-intensive process that requires the preparation and execution of many steps. Developers must work with vast information at each stage and rely on significant computing power. Let's consider each of the stages.

Dataset and data cleaning

Any language model starts with data—the more diverse and high-quality the textual information, the better the results. Data is collected from books, articles, websites, and other sources.

The raw material is often full of errors, extra characters, and duplicates, so it is essential to clean the data. After that, the text is converted into a form the model can understand — tokens. These are words, parts of words, or even individual characters that become the building blocks for training the model.

After careful preparation, the data is divided into three parts: one will be used for training, the second for testing, and the third for validation.

Designing the model architecture

Once the data is prepared, the next step is to choose the model architecture. The architecture determines how the model will process the text and how effectively it can identify relationships between words and build logical responses. Modern LLMs typically use a transformer architecture that considers each word's context in a sentence.

Determining the model size is essential: compact options are suitable for limited resources, while larger models require powerful servers and significant computing power. At this stage, key parameters such as the number of layers, attention heads, and hidden dimensions are also configured.

Training with and without a teacher

The process begins with so-called unsupervised learning. At this stage, the model learns the structure of the language, trying to predict the next element of the text, which helps it understand syntax, grammar, and contexts. This process is like immersing yourself in a new language without a dictionary: the model learns to notice patterns.

After learning the principles, training with a teacher is connected. Here, the model is trained on specific examples, which set the correct answers for certain tasks, such as summarizing text or classification. This approach allows you to make the model more accurate and specialized.

It's common to have a third phase, reinforcement learning, which encourages desired behavior and discourages unwanted outputs.

Debugging and additional training

After training, the model goes through a testing phase. It is tested on actual tasks, analyzing how efficiently and quickly it performs its work. If weaknesses are identified, the model can be further trained on narrower and more specific data.

Further support is a critical stage. After the model is released, it is advised to train it with current data and constantly improve it based on user feedback. However, you can use other approaches (like RAG) instead.

How The LLM Works

To fully understand the LLM workflow, let's examine the main stages that models go through before creating complex answers and predictions.

How the LLM learns

LLM training is based on predicting the next token. For this, a transformer architecture with an attention mechanism is used, which allows the model to emphasize the significance of some text elements over others. The model studies various texts from various sources to form generalized language representations.

An important step is the creation of vector representations (embeddings), which convert words and their contexts into multidimensional numerical vectors. For example, the words "boat," "ship," and "cutter" will be close to each other but far from the words "server," "disk," and "cable." This approach allows the model to recognize similarities, differences, and relationships between words, creating the basis for understanding the language.

Each token, along with its vector and attention weights, passes through multiple layers of the transformer, where dependencies of different levels are revealed at each stage. The model captures increasingly complex patterns with each new layer, from basic grammatical structures to high-level abstractions, refining its text representation. During training, the model processes data in batches and updates its internal parameters using backpropagation and gradient descent. A loss function, such as cross-entropy, measures prediction errors, helping the model refine its accuracy over time.

How the LLM understands the meaning of text

Using numerical vectors and an attention mechanism, the model identifies which parts of the text are interconnected and what it should focus on to perceive the phrase's meaning correctly. For example, the model should understand that the expression "Mike gave Ann flowers" is different from "Ann gave Mike flowers." Both situations are possible, but the model must determine which of the cases is meant based on the context.

One of the model's key capabilities is its ability to understand complex dependencies in long texts, considering not only the nearest words but also those far from each other. This helps to correctly interpret even confusing sentences where the meaning of a word depends on a remote context.

How the LLM generates text

The text generation process is based on a user request and the connections identified during the training phase. Based on this, the model predicts the next token that will most likely match the text's continuation until it completes the answer.

Text generation in LLMs depends on setting parameters that control the diversity and quality of the response. Different strategies are used depending on the task.

• Greedy search selects the most probable token at each step, suitable for accurate and predictable answers.
• Sampling with temperature adds an element of randomness: at low temperatures, the text becomes more meaningful and natural, and at high temperatures, it becomes more diverse and creative.
• The top-K and top-P strategies limit the choice of tokens: leave only the K most probable words or limit the choice by the total probability P, creating a balance between logic and originality of the text.

Limitations and Risks

The use of large language models is associated with several limitations and risks that are important to consider.

Unreliability of responses

LLMs can produce information that looks very plausible but is false or fabricated. This phenomenon is called "AI-hallucination". For example, a model can invent facts, references, or even whole concepts, so double-checking information for accuracy is critical.

Transparency issues

The complex structure and massive amount of data that LLMs are trained on make it difficult to understand why the model gave a particular answer. This can cause significant risks in critical areas such as healthcare and law.

Ethical issues

Models can inherit biases or errors from the data they were trained on, leading to the risk of incorrect answers or discrimination. The model may also accidentally reproduce confidential data from the training dataset.

High costs

Operating LLMs requires significant computational resources and electricity costs, making them expensive to develop and manage.

Vulnerability to manipulation

Attackers can use the model to create phishing content. For example, it can be trained in the communication style typical of bank employees and generate plausible letters that mislead customers.

LLMs are a tool with great potential, but it must be used consciously, with the risks assessed and measures taken to minimize them.

Data Security and Protection

Confidential information can be hidden behind every request sent to the model. A comprehensive approach to this matter is crucial to mitigate the potential for leaks.

Encryption is the first shield on the way to data protection. For example, every byte of information transmitted or stored must be encrypted using AES.

Access Control: only authorized employees should be able to interact with sensitive data, and multifactor authentication and activity logging help track and prevent potential breaches.

Compliance with standards can significantly reduce legal and reputational risks. Companies also use the PCI DSS standard to handle payment data.

Of course, no system can exist without constant monitoring. Automated threat monitoring systems, regular audits, and security testing are necessary to prevent potential risks.

How to Assess the Prospects of Using a LLM in a Product

Before implementing a LLM into your product, it is essential to analyze key factors. This will help you understand how well the technology suits your needs.

• Defining goals and objectives. Start with a clear understanding of what problems you want to solve with the LLM. Are you planning to automate customer support, generate content, or analyze data? Each task requires different approaches and has its peculiarities. Understanding this will allow you to choose the optimal solution.
• Quality and volume of training data. Successful training of LLM models requires a significant amount of text data, which must be high-quality, diverse, and relevant. A lack of data can negatively affect the model's effectiveness.
• Generation of false information. LLMs can generate unreliable answers. Ask yourself: how critical is it for your product if the user encounters such a problem? If this can cause serious harm, you should consider the feasibility of using a LLM in the project.
• Evaluation of resources and expected effect. Before implementing a LLM, evaluate the financial and time costs for setting up, training, and supporting the model. The benefits of product improvement may not always justify the costs of implementation.

Optimizing LLM Workflows with the Right Tools

Various solutions can streamline working with LLMs from initial experimentation to full-scale deployment.

• ML platforms provide pre-configured infrastructure with GPU support, enabling efficient model training and deployment. Built-in frameworks help standardize workflows and accelerate development.
• Inference platforms simplify the deployment and scaling of trained models using open-source solutions. They allow for seamless updates and continuous request processing without interruptions.
• GPU-powered computing offers cloud and dedicated server options, delivering the necessary computational power for training and real-time inference of even the most complex models.

By leveraging these solutions, businesses and researchers can enhance performance, reduce development time, and efficiently scale LLM applications.

Conclusion

Large language based models offer excellent prospects in natural language processing, but successful implementation requires a deep understanding of their capabilities and limitations. The right approach to model building, training, and data selection can achieve the desired results.