Large language models (LLMs) have become an incredible technology that's changing how we interact with computers. Whether you've asked ChatGPT to write a poem or used Claude to help with homework, these AI assistants have quickly become part of many people's digital lives. In this blog post, we'll explore how these amazing tools work, compare the approaches of OpenAI and Anthropic, and show you practical ways anyone can use them - even without technical knowledge.

Before we dive in, let me explain what an LLM actually is: it's a type of artificial intelligence that has been trained on massive amounts of text to understand and generate human language^[1]. Think of it like an incredibly advanced text prediction system - similar to your phone's autocomplete, but millions of times more powerful.

How Do Large Language Models Actually Work?

To understand LLMs in simple terms, let's break down the key parts of how they function:

The Building Blocks: Transformers

Most modern LLMs are built using what's called a "transformer" architecture^[1:1]. Unlike older AI systems that processed text one word at a time (like reading a book from start to finish), transformers can look at entire paragraphs simultaneously, which makes them much more efficient^[2].

Transformers have two main parts:

1. An encoder that reads and processes your text
2. A decoder that produces the response^[3]

What makes transformers special is their ability to understand relationships between words, even when they're far apart in a text. This is called "self-attention"^[2:1][4]. For example, in the sentence "The trophy wouldn't fit in the suitcase because it was too big," a transformer can understand that "it" refers to "the trophy" not "the suitcase" based on context.

Breaking Down Language: Tokenization

When you type a question to an LLM, it doesn't process your words directly. Instead, it breaks your text into smaller pieces called "tokens"^[5].

Tokens can be whole words, parts of words, or even single characters. For example, the sentence "I heard a dog bark loudly at a cat" might become individual tokens: "I," "heard," "a," "dog," "bark," "loudly," "at," "a," "cat"^[5:1].

Each token gets assigned a unique ID number, which is how the computer understands your text. This conversion of words to numbers is necessary because computers understand numbers, not words^[5:2].

Learning From Data: Training

LLMs are trained on massive collections of text - often billions of examples of human-written content from books, websites, and articles^[1:2][6]. During training, the model learns patterns by repeatedly trying to predict what word should come next in a sequence^[7].

Imagine if you read millions of books and articles - you'd get pretty good at guessing what word might come next in a sentence! That's essentially what these models are doing, but on a much larger scale.

This training process requires enormous computing power - sometimes thousands of specialized computer chips running for weeks or months^[6:1]. The more text the model processes, the better it gets at understanding language.

OpenAI vs. Anthropic: Two Different Approaches

Two of the biggest companies developing LLMs are OpenAI (creators of ChatGPT and GPT models) and Anthropic (creators of Claude). While they're building similar technology, their approaches and priorities differ in interesting ways.

OpenAI's Approach

OpenAI was founded by Elon Musk and Sam Altman with a focus on AI safety and ethics^[8]. Their GPT (Generative Pre-trained Transformer) models have become some of the most widely used LLMs in the world.

OpenAI tends to emphasize innovation and accessibility^[8:1]. Their models are trained on a broad range of internet data, which gives them flexibility but sometimes leads to challenges with content moderation^[8:2].

The company has created increasingly powerful models, from GPT-3 to GPT-4, with each version showing improved abilities in understanding language, solving problems, and generating code^[8:3].

Anthropic's Approach

Anthropic was founded by former OpenAI researchers who wanted to focus more intensely on AI safety^[9][10]. Their main product is Claude, which is designed with an emphasis on being helpful, harmless, and honest.

What makes Anthropic different is their "Constitutional AI" approach^[9:1]. They've developed a set of principles (a "constitution") that guides Claude's behavior and responses^[9:2]. This includes 75 points partly based on documents like the UN Universal Declaration of Human Rights^[9:3].

Anthropic's Claude models are known for their conversational abilities, clear explanations of their reasoning, and strong safety features that prevent harmful outputs^[11][10:1].

Key Differences

Based on the comparison, here's how the two companies differ:

As one comparison puts it: "Anthropic goes slow and steady, putting up safeguards, while OpenAI races ahead to find new things. Both ways have good points"^[8:4].

How People Are Using LLMs in Their Daily Lives

These powerful AI tools have quickly become part of many people's everyday routines:

At Work

• Customer service: Companies use Claude and GPT models to provide 24/7 customer support^[12]
• Legal assistance: Lawyers use LLMs to analyze legal documents and summarize cases^[12:1]
• Programming help: Developers use LLMs to help write and debug code^[11:1][12:2]
• Data processing: Businesses use LLMs to extract information from documents and summarize survey responses^[12:3]

For Personal Use

• Homework help: Students ask LLMs to explain difficult concepts or brainstorm essay ideas
• Creative writing: People use LLMs for everything from drafting emails to writing stories and poetry
• Learning new topics: LLMs can explain virtually any subject in simple terms
• Summarizing content: People paste long articles or videos and ask for brief summaries

Special Features

• Image analysis: The newest models from both companies can analyze pictures and describe what's in them^[11:2][10:2]
• Translation: LLMs can translate text between multiple languages with impressive accuracy^[11:3]
• Data visualization: Some models can help create charts and graphs from data

Tips for Non-Technical People

If you're not a technology expert, here are some ways to make the most of these powerful tools:

Writing Effective Prompts

The way you ask questions greatly affects the quality of answers you'll get:

• Be specific about what you want
• Provide context and background information
• Mention the format you want (bullet points, paragraphs, etc.)
• If you don't get a helpful answer, try rewording your question

Understanding Limitations

It's important to know what LLMs can't do well:

• They don't truly "understand" text like humans do - they're making statistical predictions
• They can make up information (often called "hallucinations")
• They only know information up to their training cutoff date
• They may reflect biases present in their training data

Getting Started: Simple Ways to Use LLMs

Here are five easy ways for anyone to start using LLMs:

1. Writing assistant: Ask for help drafting emails, letters, or social media posts
2. Learning tool: Ask the LLM to explain complicated concepts in simple terms
3. Research helper: Ask for summaries of topics you're interested in
4. Creative partner: Get help brainstorming ideas for stories, art projects, or gifts
5. Personal organizer: Ask for help creating schedules, to-do lists, or meal plans

Recent Discoveries About How LLMs Work

Scientists are constantly learning more about how these AI systems function. Anthropic recently announced they've identified how millions of concepts are represented inside their Claude model^[13]. This could help make these systems more transparent and trustworthy.

Anthropic researchers have also developed ways to "peer inside" their models and watch what happens as they generate responses^[14]. This is helping them understand the strange and sometimes surprising ways LLMs solve problems.

The Future of LLMs

These AI systems are evolving rapidly. Companies are working on giving LLMs more capabilities, such as directly controlling computers to perform complex tasks^[12:4]. The newest Claude model (Claude 3.7 Sonnet) is described as having "hybrid reasoning" abilities that can solve problems with careful step-by-step thinking^[12:5].

As LLMs continue to improve, they'll become even more helpful for both technical and non-technical users. New features like longer memory, better reasoning, and improved factual accuracy are already being developed.

Conclusion

Large Language Models represent one of the most significant technological breakthroughs of recent years. By understanding the basics of how they work and the differences between leading providers like OpenAI and Anthropic, anyone can take advantage of these powerful tools.

Whether you're using them for work, school, or personal projects, LLMs offer capabilities that were unimaginable just a few years ago. As they become more integrated into our digital lives, their impact will only grow.

Remember that these are tools designed to assist and enhance human abilities - not replace them. By understanding their strengths and limitations, you can use them effectively to help with all kinds of tasks, even if you have no technical background.

Have you tried using ChatGPT or Claude? What's been your experience with these AI assistants? Share your thoughts and favorite use cases in the comments below!