What is an LLM?
A Large Language Model is a type of deep machine learning algorithm that processes, analyzes, and generates natural language text. The word "large" refers to two dimensions: the scale of training data (hundreds of billions of words) and the number of model parameters — the internal numerical coefficients of the neural network whose values are determined during training. Models in the GPT family from OpenAI, Claude from Anthropic, and Gemini from Google DeepMind range from tens of billions to trillions of such parameters.
LLMs belong to the broader category of generative AI, but do not encompass it entirely. Generative AI also includes systems that create images, audio, and video. LLMs are a specialized subset of that category — focused exclusively on text and code. Every large language model is generative AI, but not every generative AI system is a language model.
LLM technology is not a standalone AI model in the colloquial sense — it is a foundation on which specific products are built. ChatGPT, GitHub Copilot, and the Claude application are interfaces built on top of base language models, wrapped in fine-tuning pipelines, moderation systems, and infrastructure.
Who is behind it?
The modern LLM architecture traces back to a landmark 2017 paper by Google Research scientists — Ashish Vaswani and collaborators — titled "Attention Is All You Need," which introduced the Transformer architecture. That architecture underpins all leading language models today.
Commercial LLMs are currently developed primarily by: OpenAI (GPT family), Google DeepMind (Gemini), Anthropic (Claude), Meta AI (LLaMA — open-source), Mistral AI, and Chinese companies such as Alibaba (Qwen). Poland has produced its first local initiatives — the Bielik and PLLuM models, designed for Polish language and cultural context. In 2024, a second wave of Chinese companies emerged — most notably DeepSeek (models DeepSeek-V3 and R1), which matched OpenAI-level performance at a fraction of the training cost.
How does it work?
At the most fundamental level, an LLM operates as a sophisticated next-token prediction engine. A token is a unit of text — it may correspond to a word, a subword fragment, or a punctuation mark. Given an input sequence of tokens (the user's prompt), the model computes a probability distribution over its entire vocabulary and selects the next token. It repeats this process iteratively to generate a response.
An important technical constraint is the context window — the maximum number of tokens a model can process in a single pass. Early models supported 4,000 tokens; newer ones (GPT-4, Claude) range from 128,000 to one million. Tokens outside the context window are invisible to the model — which is why very long documents require chunking, and why a model does not “remember” conversations from hours earlier without an external memory mechanism.
The key difference from earlier NLP systems lies in the attention mechanism. Traditional recurrent networks (RNN, LSTM) processed text sequentially — word by word — and lost context in long passages. The Transformer architecture processes all tokens simultaneously and computes, for each token, how much "attention" it should pay to every other token in the sequence. The Self-Attention mechanism (multi-head attention) allows the model to simultaneously track multiple types of relationships: grammatical, semantic, referential.
The model does not "understand" text in a human sense. It operates on statistical dependencies between tokens encoded in billions of parameters. The apparently intelligent responses are a product of the depth of those dependencies — not of consciousness or intent.
What are its key components?
An LLM is built from several technical layers — each responsible for a different aspect of language processing:
Transformer architecture — the model's backbone, composed of encoder and/or decoder layers. Most generative models today (GPT-4, Claude, Gemini) use a decoder-only architecture: they process the context sequence and autoregressively generate subsequent tokens.
Parameters — numerical coefficients of the neural network optimized during training. GPT-3 had 175 billion parameters; newer models reach one trillion and beyond. Research by Kaplan et al. (2020) showed that model performance follows Scaling Laws — a power function of parameter count, compute budget, and data volume.
Tokenization — the process of splitting text into tokens before feeding it to the model. This determines how the model "sees" language, including the handling of rare words, numbers, and code.
Embeddings (vector representations) — after tokenization, each token is converted into a numerical vector that encodes its meaning in a high-dimensional space. These vectors are what the model actually computes on: semantically similar words are located close to each other in this space, enabling the model to capture relationships between concepts. Without embeddings, tokenization would be nothing more than a sequence of numbers with no semantic structure. More in the insight: Embeddings in AI — how machines understand the meaning of words.
Training data — corpora of text from the internet, books, scientific articles, and code. The scope and quality of this data directly shapes the model's capabilities and limitations.
Overview diagram
The following diagram shows the three-stage lifecycle of a production-ready LLM: from Pretraining on raw text data, through Supervised Fine-Tuning, to Reinforcement Learning from Human Feedback. Based on Vaswani et al. (2017) and OpenAI's publicly documented methodology for the GPT model family.
What can it be used for?
The range of LLM applications is wide, but it is worth distinguishing mature use cases from experimental ones.
Mature and production-ready: text generation and editing (product descriptions, document summaries, emails), code assistants (GitHub Copilot, Cursor) — autocompletion, refactoring, error explanation, machine translation that preserves context and nuance, customer service chatbots capable of handling unstructured queries, sentiment analysis and information extraction from large text datasets.
Actively developing: Agentic AI systems (LangGraph, AutoGPT) — models making autonomous decisions and calling external tools, Retrieval-Augmented Generation — LLM connected to a company knowledge base minimizing hallucinations, multimodal models (Gemini, GPT-4o) — combining text analysis with image, audio, and video.
How does it differ from other approaches?
Before the Transformer era, NLP systems relied on hand-crafted rules, n-gram models, and classifiers (Naive Bayes, SVM). They required extensive feature engineering and were highly inflexible — they performed well on narrow tasks but generalized poorly.
LLMs fundamentally change this: a single Foundation Model (base model), trained on a massive corpus, can perform hundreds of different tasks with minimal or no fine-tuning (few-shot learning). Knowledge transfer across domains — impossible in traditional systems — is a natural property of the architecture.
The difference relative to earlier neural approaches (LSTM, RNN) is equally fundamental: parallelized computation in the Transformer enables training at previously inaccessible data scales, and the attention mechanism dramatically improves long-context handling.
Key limitations and challenges
Hallucinations are the largest structural problem in LLMs. The model generates false information with the same confidence as true information — because it does not "know" what is factually correct; it predicts statistically likely tokens. Even high mathematical certainty (low entropy) in a prediction does not guarantee factual accuracy. RAG pipelines and temperature = 0 significantly reduce this problem in production deployments, but do not eliminate it entirely.
Compute and environmental costs — training a large model consumes months of GPU cluster operation and enormous amounts of electricity. The operational costs of running a model in production (inference) remain significant even after training is complete.
Bias — models trained on raw internet data absorb the statistical patterns of inequality and misinformation present in that data. Without careful fine-tuning and RLHF, a model may generate biased, harmful, or incorrect judgments.
Security — models fine-tuned via LoRA (Low-Rank Adaptation) can be theft targets — a compressed file of secondary training weights is only a few megabytes in size yet contains a company's proprietary knowledge. Model drift (model behavior shifting under adversarially crafted inputs) is another attack vector in production systems.
Limited interpretability — engineers can observe model outputs, but fully tracing why a specific token was selected across billions of parameters is not feasible. This is an epistemological barrier to diagnostics and certification in high-stakes systems.
Why does it matter?
LLMs have shifted the boundary of what can be automated. For decades, automation covered repetitive and structured tasks — manufacturing, logistics, simple office operations. LLMs are the first technology to effectively enter the domain of cognitive work: document analysis, code writing, customer support, translation, and knowledge summarization.
This has consequences for labor markets, education, and how organizations are structured — but also for digital infrastructure. Every organization deploying LLM-based systems today must consciously navigate three tensions: between usefulness and reliability (hallucinations), between capability and cost (scaling), and between personalization and data security (fine-tuning vs. data leakage risk).
Understanding what an LLM is architecturally — not just as a product interface — enables decisions grounded in facts rather than marketing. The difference between a base model and a fine-tuned product, between high-temperature inference and deterministic RAG, between GPT-4 and a locally deployed LLaMA — these are not engineering details for specialists. They are variables that determine whether a system will meet business requirements.
LLMs are not a product — they are a class of foundational technology, much like relational databases or network protocols. User interfaces change every few months, but the Transformer architecture and its consequences — the statistical nature of prediction, scaling costs, the hallucination problem — are constants that every AI practitioner should understand.