What Is an LLM? How Large Language Models Work, Top Models & Use Cases

ChatGPT, Claude, Gemini — you hear these names every day. But do you know what technology actually powers them? It's called an LLM (Large Language Model), and understanding it is the key to using AI tools effectively.

In this guide, we'll explain what an LLM is in plain language, how it works under the hood, which models lead the market in 2026, and what limitations you need to watch out for. Everything you need to understand LLMs, all in one place.

1. What Is an LLM? — The Short Answer

A Large Language Model (LLM) is an AI system trained on massive amounts of text data that can understand and generate human-like language.

Let's break down the name:

• "Large": Trained on trillions of words from websites, books, research papers, and more
• "Language": Specialized in processing and generating text
• "Model": A mathematical system that takes input and produces output — essentially the AI's "brain"

ChatGPT runs on OpenAI's GPT series, Claude runs on Anthropic's Claude series, and Gemini runs on Google's Gemini series. In other words, an LLM is the engine that powers tools like ChatGPT and Claude.

A Simple Way to Think About It

At its core, an LLM works by predicting the next word — a surprisingly simple concept.

When you type "The weather today is," the model calculates the probability of words like "sunny," "cloudy," or "rainy" coming next, based on patterns learned from its training data. It picks the most likely continuation and repeats this process thousands of times to build complete sentences, paragraphs, and even entire essays.

2. How LLMs Work — 3 Key Steps

Here's how an LLM goes from raw data to generating useful responses, in three stages.

Step 1: Pre-training

The model ingests a massive corpus of text — web pages, books, academic papers, Wikipedia, and more — spanning trillions of tokens (word units). During this phase, it trains by repeatedly predicting the next word in a sequence.

For example, given "To be or not to ___," the model learns to predict "be." By doing this trillions of times, it absorbs language patterns, grammar, factual knowledge, and even reasoning abilities.

This stage requires thousands to tens of thousands of GPUs running for months. For a sense of scale, Stanford HAI's AI Index 2024 estimated the compute cost of training at about $78 million for GPT-4 and about $191 million for Google's Gemini Ultra (AI Index 2024). Both are estimates for 2023 models; the companies do not officially disclose training costs for newer models.

Step 2: Fine-tuning (RLHF)

After pre-training, the model can generate text but has no filter — it may produce harmful or unhelpful content. Fine-tuning uses human feedback to teach the model the difference between good and bad responses, making it safer and more useful.

This technique is called RLHF (Reinforcement Learning from Human Feedback). It's the reason ChatGPT responds politely and helpfully instead of producing raw, unfiltered text.

Step 3: Inference

When you ask a question, the LLM receives your prompt (input text) and uses its trained knowledge to generate one word at a time, always picking the most probable next word. This is why you see text appear character by character when chatting with ChatGPT or Claude.

The Foundation: Transformer Architecture

Nearly every modern LLM is built on the Transformer architecture, introduced by Google in 2017. Its breakthrough innovation is the Attention mechanism — a system that efficiently identifies which words in a sentence relate to which other words, regardless of distance.

The "T" in GPT actually stands for "Transformer."

3. The Two Camps — Closed vs Open-Weight

The LLM world is divided into two camps: closed models and open-weight models. Individual model names turn over every six months, but the difference between the two camps is structural — and the reasoning behind which one to pick stays the same year after year.

Below are the main model families, grouped by developer. Individual version names turn over every few months, so this map lists what each family is like and what to check when choosing. The two questions that decide which camp you belong in — can your data leave, and can you hold the weights yourself — do not change.

Closed-Source Models (Commercial APIs)

For a detailed comparison of pricing and features, see our Claude vs ChatGPT pricing comparison.

Open-Source Models

The key advantage of open-source models is that you can run them on your own servers, keeping your data private while still leveraging LLM capabilities. The rapid rise of Chinese-developed models like DeepSeek and Qwen has dramatically expanded open-source options.

4. LLM vs Traditional AI vs Generative AI

To put it simply: an LLM is a text-focused type of generative AI — it's a subset of the broader generative AI category. For the full picture, check out our article on what generative AI is.

5. LLM Use Cases — What Can You Do?

LLMs are remarkably versatile, and they're already being used across countless domains.

Business Applications

• Document creation: Generate drafts of reports, emails, and proposals in seconds
• Customer support: Build automated FAQ systems and intelligent chatbots
• Data analysis: Feed in CSV files for trend analysis and automated reporting
• Software development: Use tools like Claude Code or Codex for code generation and debugging

Personal Use

• Learning: Ask an LLM to explain complex topics and deepen your understanding
• Translation and language study: Get natural translations and writing corrections
• Side income: Boost your productivity in writing, image creation, and coding (see our AI side hustle guide)
• Everyday tasks: Plan trips, get recipe ideas, organize your schedule

Specialized Fields

• Healthcare: Summarize research papers, assist with diagnosis (under expert supervision)
• Legal: Review contracts, streamline case law research
• Education: Auto-generate personalized learning materials
• Research: Accelerate literature reviews and hypothesis exploration

6. Limitations and Risks

LLMs are powerful, but they're far from perfect. Here are the limitations you need to understand before relying on them.

1. Hallucination

LLMs can generate information that sounds completely convincing but is factually wrong. How often this happens varies widely by task and model, so no single percentage describes it. A 2025 paper by OpenAI researchers traces one cause to training and evaluation that reward guessing over admitting uncertainty, much as a student on a multiple-choice exam scores more by guessing than by leaving a blank (Why Language Models Hallucinate).

Countermeasure: Always verify important information against primary sources.

2. Knowledge Cutoff

An LLM's knowledge stops at whatever date its training data ends. Be sure to check each model's knowledge cutoff date, and use web search integration (RAG) for anything that requires up-to-date information.

3. Bias

Biases present in the training data — including gender, racial, and cultural biases — can show up in LLM outputs. This is especially important to watch for in contexts that require fairness, such as hiring and performance evaluations.

4. Privacy and Security

When you use a cloud-based LLM, your input is sent to the service provider's servers. Always review the data policy before entering confidential or personal information. Running open-source models on your own infrastructure is one way to mitigate this risk.

5. Cost

Using cutting-edge LLMs at scale can result in API bills of thousands to tens of thousands of dollars per month. The best practice is to start small, measure ROI, and scale up gradually.

7. What the Mechanism Tells You About How to Ask

Once you know the mechanism above (LLMs read text as fragments called tokens, pick the next word by probability, and know only what was in their training data up to a cutoff), you can predict which requests they handle badly and how to rephrase them.

All of these follow from one point: an LLM predicts how text continues, and checking the facts is the job of people and tools. To refine how you ask, see How to Write Prompts (AI Basics course); for how much you can hand over at once, see What Is a Context Window?

8. 2026 Trends — Where LLMs Are Headed

Multimodal Capabilities

LLMs are evolving beyond text to understand and generate images, audio, and video simultaneously. Leading models such as GPT, Gemini Pro and Claude Opus can answer questions about images and hold real-time voice conversations.

Smaller Models, Better Efficiency

Advances in MoE (Mixture of Experts) architecture and model compression are enabling dramatic cost reductions without sacrificing performance. Mistral Large 3 is a prime example: of its 675B total parameters, only 41B are active for any given token (Mistral's model card).

AI Agents

LLMs are moving beyond simple Q&A to become AI agents that can plan and execute multi-step tasks. Web research, understanding and modifying entire codebases, and orchestrating multiple tools — tasks that were impossible just a year ago are now a reality.

Reasoning Breakthroughs

OpenAI's GPT (topped by Astra) and Anthropic's Claude (topped by Fable) are achieving expert-level scores in mathematical reasoning and logical thinking. "Inference-time scaling" — spending more compute time at response generation to improve quality — is a major emerging trend.

The Open-Source Surge

Meta (Llama, Muse Glimmer), Alibaba (Qwen), and DeepSeek are releasing open-source LLMs that rival proprietary models. This gives organizations the option to leverage LLMs while keeping their data entirely in-house.

9. Summary

An LLM is the engine that powers tools like ChatGPT and Claude. Understanding how this engine works will make you a far more effective — and more critical — user of AI tools.

Want to build a solid AI foundation? Try our AI beginner's guide. Curious where you stand? Take our AI knowledge assessment to find out.

Once you've grasped what an LLM is, the next step is to run one yourself. Read how to run a local LLM to learn how to run AI on your own PC without the cloud.

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