What Are LLMs? Large Language Models in Plain English

If you’ve spent any time online in the past couple of years, you’ve undoubtedly encountered the work of a Large Language Model, or LLM. Maybe you asked ChatGPT to draft an email, used Copilot to help you write code, or saw a surprisingly coherent and creative story generated by an AI. These tools have exploded into the public consciousness, shifting from niche technology to mainstream applications that are reshaping how we work, create, and communicate.

But what exactly is an LLM? The term is thrown around constantly, yet for many, it remains a mysterious black box. It feels like magic—you type in a question, and a thoughtful, well-written answer appears. How does it know so much? Is it actually thinking? Is it sentient?

The short answer is no, it’s not thinking in the human sense. The longer answer is far more fascinating. At its core, an LLM is a sophisticated pattern-recognition machine, a digital brain trained on a staggering amount of text and data from the internet. It learns the relationships between words, sentences, and ideas on a scale that is impossible for a human to comprehend.

This article is your guide to understanding this revolutionary technology. We’ll strip away the jargon and explain everything in plain English. We will explore what the “large” in Large Language Model really means, how these models are trained, what they can (and can’t) do, and who the major players are in this rapidly evolving field. By the end, you’ll not only understand what LLMs are but also how to think about them, use them effectively, and appreciate the incredible engineering behind them.

The Core Idea: A Super-Powered Autocomplete

To begin, let’s use a simple analogy. Think about the autocomplete feature on your phone’s keyboard or in your email client. As you type “I’m running late, I’ll be there in about…”, it might suggest “10 minutes,” “20 minutes,” or “an hour.” It does this by having analyzed countless sentences and predicting the most statistically likely words to come next based on the context you’ve provided.

Now, imagine scaling that concept up by a factor of billions. An LLM is, in essence, a vastly more powerful and complex version of this predictive text system. Its fundamental goal is to predict the next word in a sequence. When you give it a prompt like, “Write a short story about a robot who discovers music,” it starts by predicting the most probable first word. Then, based on that first word and your original prompt, it predicts the second word. It continues this process, word by word, token by token, stringing together sentences and paragraphs that are statistically coherent and relevant to the initial request.

It isn’t “understanding” the concept of a robot or music in the way a human does. It doesn’t have feelings, memories, or consciousness. Instead, it has learned the mathematical relationships between words. It knows that the word “robot” is often associated with words like “gears,” “circuits,” and “programming,” while “music” is associated with “melody,” “rhythm,” and “harmony.” By processing your prompt, it activates these associations and generates text that aligns with the patterns it learned during its training. This predictive ability is what allows it to draft emails, write code, answer complex questions, and even compose poetry.

The “Large” in Large Language Models

The word “large” is not an exaggeration; it refers to two key aspects: the size of the model’s neural network and the massive volume of data it was trained on.

1. Model Size (Parameters): The “brain” of an LLM is an artificial neural network. The size of this network is measured in “parameters.” You can think of parameters as the knobs and dials that the model adjusts during training to fine-tune its knowledge and predictive capabilities. The more parameters a model has, the more nuance and complexity it can capture from the training data. Early models had millions of parameters, but modern LLMs have billions, or even trillions.
2. Dataset Size: To learn, these models need to be fed an enormous amount of text data. This training corpus often includes a significant portion of the public internet—websites like Wikipedia, massive digitized book collections, scientific papers, news articles, and code repositories like GitHub. The sheer volume allows the model to learn grammar, facts, reasoning styles, and the subtle connections between different topics.

Here’s a look at how the scale of these models has grown over time, using publicly available estimates for parameter counts.

Model (Family)	Developer	Approximate Parameters	Key Characteristic
GPT-2	OpenAI	1.5 Billion	Showed impressive text generation capabilities for its time.
GPT-3	OpenAI	175 Billion	A massive leap in scale, enabling a wide range of new applications.
Llama 2	Meta	70 Billion	A powerful open-source model, fostering wider research.
Gemini 1.5 Pro	Google	~1 Trillion (via MoE)	Highly efficient architecture, processing huge contexts.
GPT-4	OpenAI	>1 Trillion (via MoE)	The industry leader in reasoning and multimodal capabilities.

Note: MoE stands for “Mixture of Experts,” an architecture that allows models to use only a subset of their parameters for any given task, making them more efficient.

This immense scale is what gives LLMs their power. With more parameters and more data, they can form a more detailed and accurate internal representation of human language and knowledge.

How LLMs Are Trained: A Two-Phase Process

Creating a powerful LLM is a complex and resource-intensive process that generally involves two main stages: pre-training and fine-tuning.

Phase 1: Pre-training

This is the foundational stage where the model learns the fundamentals of language. During pre-training, the model is fed the massive dataset we discussed earlier—terabytes of text. The learning process is “unsupervised” or “self-supervised,” meaning it doesn’t require humans to manually label the data.

A common pre-training task is “masked language modeling.” The algorithm takes a sentence, randomly hides (or “masks”) a word, and then tasks the model with predicting the missing word based on the surrounding context. For example, given the sentence, “The cat sat on the ____,” the model would learn to predict the word “mat.”

By repeating this process billions of times with countless variations, the model builds a deep, statistical understanding of:

• Grammar and syntax.
• Facts about the world (e.g., “The capital of France is Paris”).
• Semantic relationships (e.g., “king” is to “queen” as “man” is to “woman”).
• Context and nuance in language.

After pre-training, the model is a vast repository of general knowledge, but it’s not yet very good at being a helpful assistant. It’s like someone who has read an entire library but doesn’t know how to have a conversation.

Phase 2: Fine-Tuning

This is where the model learns to be useful, safe, and aligned with human intent. Fine-tuning involves training the model on a smaller, high-quality dataset that has been curated by humans. This process often uses a technique called Reinforcement Learning from Human Feedback (RLHF).

Here’s a simplified breakdown of how RLHF works:

1. Supervised Fine-Tuning: Human AI trainers create a dataset of high-quality conversations. They write prompts and the ideal responses they would want the AI to provide. The model is trained on this dataset to learn how to follow instructions and respond in a conversational style.
2. Reward Modeling: The AI is given a single prompt and generates several different responses. A human trainer then ranks these responses from best to worst. This data is used to train a separate “reward model” whose job is to predict which responses a human would prefer.
3. Reinforcement Learning: The LLM then generates responses to new prompts, and the reward model scores them in real-time. The LLM’s algorithm is adjusted to maximize this reward score, effectively teaching it to generate responses that are more helpful, harmless, and honest.

This fine-tuning process is what transforms a raw, pre-trained model into a polished product like ChatGPT or Claude, capable of following complex instructions and avoiding harmful outputs.

The Architecture Behind the Magic: Transformers

The technological breakthrough that enabled modern LLMs is an architecture called the “Transformer,” introduced in a 2017 paper by Google researchers titled “Attention Is All You Need.” Before the Transformer, models processed text sequentially, word by word, which made it difficult to keep track of context in long sentences.

The Transformer’s key innovation is the “attention mechanism.” This mechanism allows the model to weigh the importance of all the words in the input text simultaneously, regardless of their position. When processing a sentence, it can “pay attention” to the most relevant words to understand the context and meaning.

For example, in the sentence, “The robot picked up the screwdriver because it was heavy,” the attention mechanism helps the model understand that “it” refers to the “screwdriver,” not the “robot.” In the sentence, “The robot picked up the screwdriver because it needed to be repaired,” attention helps it understand that “it” now refers to the “robot.” This ability to handle long-range dependencies and context is what makes the Transformer architecture so powerful and is the foundation upon which nearly all modern LLMs are built.

What Can LLMs Do? Key Capabilities and Use Cases

Large Language Models are incredibly versatile. Their ability to understand and generate text has unlocked a vast array of applications across numerous industries. Here are some of the most common capabilities.

• Content Creation: Writing blog posts, marketing copy, emails, social media updates, and even creative fiction.
• Summarization: Condensing long articles, research papers, or meeting transcripts into concise summaries.
• Question Answering: Acting as a conversational search engine, providing detailed answers to complex questions.
• Code Generation: Writing code snippets, debugging existing code, and translating code between different programming languages.
• Translation: Translating text between dozens of languages with increasing accuracy and nuance.
• Data Extraction: Pulling structured information from unstructured text, such as extracting key details from a contract.

Here’s a table of common use cases and some of the tools that excel at them:

Use Case	Description	Popular Tools
Conversational AI	Engaging in open-ended dialogue, answering questions, and acting as a virtual assistant.	ChatGPT, Claude, Gemini, Copilot
Content & Copywriting	Generating high-quality marketing copy, blog posts, and other written materials.	Jasper, Copy.ai, Writesonic
Coding Assistance	Writing, completing, and debugging code directly within a developer’s editor.	GitHub Copilot, Amazon CodeWhisperer
Writing Enhancement	Improving grammar, style, and clarity in existing text.	Grammarly, QuillBot
Brainstorming & Ideation	Acting as a creative partner to generate ideas for projects, business plans, or stories.	ChatGPT, Notion AI

Meet the Major Players: A Look at Prominent LLMs

The field of LLMs is dominated by a few key technology companies and research labs, each with its own flagship models. While there are hundreds of models, a few stand out for their performance and widespread adoption.

Model Name	Developer	Key Features	Primary Application
GPT Series (GPT-4)	OpenAI	State-of-the-art reasoning, creativity, and multimodal capabilities.	Powering ChatGPT and Microsoft Copilot.
Gemini Family	Google	Natively multimodal, designed to process text, images, audio, and video.	Integrated into Google products like Gemini and Google Workspace.
Claude Family	Anthropic	Strong focus on safety and constitutional AI. Excellent for dialogue.	Available via its own chat interface and API.
Llama Family	Meta	High-performance open-source models that can be freely modified.	A popular foundation for custom, fine-tuned applications.

The Limitations and Challenges of LLMs

Despite their incredible capabilities, LLMs are not perfect. It’s crucial to understand their limitations to use them responsibly and effectively.

• Hallucinations: Because LLMs are predictive models, they can sometimes generate text that sounds plausible but is factually incorrect or nonsensical. This is often called “hallucinating.” They may invent facts, cite non-existent sources, or make logical errors. Always fact-check critical information generated by an LLM.
• Bias: LLMs are trained on data from the internet, which contains a wide range of human biases. As a result, the models can inadvertently perpetuate or even amplify stereotypes related to gender, race, and culture. Companies are actively working to mitigate these biases, but it remains a significant challenge.
• Lack of True Understanding: An LLM does not “know” or “understand” things in the way humans do. It is a pattern-matching engine. It lacks common sense, consciousness, and the ability to truly reason from first principles. Its intelligence is a reflection of its training data, not genuine comprehension.
• Data Privacy: When you use a public LLM service, your conversations may be used to further train the model. This raises privacy concerns, especially when dealing with sensitive personal or proprietary business information. Many services now offer business tiers with stricter data privacy policies.
• Computational Cost: Training and running large-scale LLMs requires immense computational power, which consumes a significant amount of energy and has a substantial environmental footprint. Researchers are working on more efficient architectures to address this.

The Future of Large Language Models

The field of LLMs is advancing at an astonishing pace, and the future promises even more transformative capabilities. Here are a few key trends to watch:

• Multimodality: The next generation of models will be natively multimodal, meaning they can seamlessly process and reason about information from different sources, including text, images, audio, and video. You might be able to show a model a video of a car engine making a strange noise and ask it to diagnose the problem.
• Personalization and Specialization: We will see more specialized models trained for specific domains, such as medicine, law, or finance. We will also see smaller, more efficient models that can run locally on your personal devices, offering greater privacy and personalization.
• Agency and Automation: LLMs will increasingly be integrated into “agent” systems that can perform multi-step tasks on your behalf. For example, you could ask an AI agent to “plan a weekend trip to San Francisco for me and my two friends, find a pet-friendly hotel under $300 a night, and book us a table at a highly-rated Italian restaurant.” The agent would then use various tools and APIs to execute these tasks.
• Deeper Integration: Expect to see LLM capabilities embedded into nearly every piece of software you use, from your operating system and web browser to productivity suites like Microsoft Teams and design tools like Figma.

Conclusion: Your New Superpower

Large Language Models are no longer a futuristic concept; they are powerful, practical tools available to everyone. We’ve journeyed from a simple autocomplete analogy to the complex architecture of Transformers, from the massive scale of pre-training to the human-guided nuance of fine-tuning. We’ve seen that LLMs are not magical thinking machines but sophisticated pattern predictors trained on the collective knowledge of the internet.

Understanding how they work—their strengths and their weaknesses—is the key to unlocking their potential. They can be incredible assistants for brainstorming, powerful engines for creativity, and tireless helpers for tedious tasks. However, they are also prone to errors, reflect our own biases, and lack true understanding. By approaching them with a healthy mix of curiosity and critical thinking, you can leverage them as a true superpower.

This technology is still in its early days, and its impact will only continue to grow. Now that you understand the fundamentals in plain English, you are equipped to navigate this exciting new landscape.