A large language model (LLM) is a type of artificial intelligence trained on enormous quantities of text — books, websites, code, scientific papers — to understand and generate human language. LLMs are the technology inside ChatGPT, Claude, Gemini, Qwen, and virtually every AI assistant and coding tool available today.

How an LLM learns

LLMs are built on a class of neural network called a transformer, first described in the landmark 2017 Google paper “Attention Is All You Need.” The key innovation was self-attention: the model learns how every word in a sentence relates to every other word, allowing it to grasp context across long passages rather than just adjacent words.

Training works by feeding the model billions of sentences and asking it, repeatedly, to predict the next word. The model guesses, checks whether it was right, adjusts its internal settings — called parameters or weights — and moves to the next sentence. After trillions of such adjustments across terabytes of data, the model has internalized grammar, facts, coding patterns, and a basic capacity to reason.

The word “large” refers to scale: early models had millions of parameters; today’s frontier models have hundreds of billions, or more.

How it generates text

When you send a prompt, the model doesn’t retrieve a stored answer. It reads your text as a sequence of tokens (roughly word fragments), uses its learned weights to calculate probabilities over every possible next token, and samples from those probabilities to build a response — one token at a time.

This is why LLMs can sound confident while being wrong: they are pattern-matching engines, not fact-checkers. When a model produces a plausible-sounding but incorrect answer, that is called hallucination — a well-known limitation of all current LLMs.

What LLMs can — and can’t — do

LLMs excel at language tasks: writing, summarizing, translating, coding, answering questions, and drafting documents. Their weaknesses include real-time information (they have a training cutoff date and cannot browse the internet unless given a dedicated tool), arithmetic (they frequently make errors in calculation), and factual accuracy in highly specialized domains.

The easiest way to try an LLM today is to visit ChatGPT — no installation required. For a step-by-step introduction, see our complete beginner’s guide to ChatGPT. More specific models — such as Google Gemini and Alibaba’s Qwen — are also freely accessible.

Why it matters for Georgia

Georgian is a linguistically distinct language with its own unique script, but it has a relatively small presence in the digital text that LLMs train on — which is overwhelmingly English. In practice, this means LLMs tend to produce less accurate and less fluent Georgian text than English, and may occasionally switch languages mid-response.

The gap is gradually narrowing as more Georgian-language content appears online, and some specialized models are now fine-tuned on Georgian data. For Georgian businesses and developers, this dual reality is worth keeping in mind: a current limitation to work around, and an opportunity for those who invest in Georgian-language AI tooling now.

FAQ

What is the difference between an LLM and ChatGPT?
ChatGPT is a product built on top of an LLM (OpenAI’s GPT-4 series). The LLM is the underlying model; ChatGPT is the interface and service wrapped around it.

How many parameters does a typical LLM have?
Smaller open models — such as Meta’s Llama 3.1 8B — have around eight billion parameters. Frontier models like GPT-4 and Claude are estimated in the hundreds of billions. Parameter count alone does not determine quality.

Can an LLM understand Georgian?
Yes, to a degree — but most LLMs have seen far less Georgian text during training than English, so Georgian output can be less accurate. Performance is improving as Georgian-language content online grows.

What does “training cutoff” mean?
An LLM only knows information present in its training data up to a specific date. Without a built-in browsing tool, it cannot access current events or news published after that cutoff.

Sources: Large language model — Wikipedia · How Do Large Language Models Work? — Coursera