Large Language ModelsThe Brains Behind ChatGPT
LLMs are AI systems that learned to understand and generate human language by reading most of the internet. They power ChatGPT, Claude, and other AI assistants — and you don't need a tech background to use them effectively.
What Is an LLM?
What exactly is a Large Language Model?
An LLM is an AI system trained on massive amounts of text — books, websites, articles — to understand and generate human language. Think of it as autocomplete on steroids: it predicts what words should come next, but it's so good at it that it can write essays, answer questions, and even code.
Why are they called "large"?
The "large" refers to the size of the model — how many parameters (adjustable settings) it has. GPT-4 has hundreds of billions of parameters. More parameters generally means the model can understand more nuances and handle more complex tasks.
How do LLMs learn?
LLMs learn by reading billions of text examples and finding patterns. They figure out which words tend to follow other words, how sentences are structured, and even how concepts relate to each other. It's like learning a language by reading every book ever written.
How Do LLMs Work?
The process is simpler to understand than you might think
Training on Text
The model reads massive amounts of text — trillions of words from books, websites, and documents.
Analogy: Like learning to cook by reading every cookbook ever written.
Finding Patterns
It learns patterns: what words go together, how ideas connect, what makes sense grammatically and logically.
Analogy: Like noticing that "peanut butter" is usually followed by "and jelly."
Predicting Words
Given some text, it predicts the most likely next word. Then it predicts the next word after that, and so on.
Analogy: Like your phone's autocomplete, but predicting entire paragraphs.
Fine-tuning
The model is further trained with human feedback to be helpful, harmless, and honest.
Analogy: Like a chef learning from customer reviews to improve their dishes.
Popular LLMs You Can Try
The major AI companies each have their own LLMs
GPT-4 / GPT-4o
OpenAI
The model behind ChatGPT. Known for being versatile, creative, and good at following complex instructions.
Claude 3.5
Anthropic
Designed with safety in mind. Excellent at analysis, coding, and having natural conversations.
Gemini
Google's multimodal model that can understand text, images, and more. Integrated with Google services.
Llama 3
Meta
Open-source model you can run on your own computer. Powers many other AI applications.
What Can LLMs Do?
LLMs are surprisingly versatile
Conversation
Chat naturally, answer questions, explain concepts
- •Customer support
- •Tutoring
- •Brainstorming partner
Writing
Create articles, emails, stories, reports
- •Blog posts
- •Marketing copy
- •Technical documentation
Analysis
Summarize documents, extract insights, compare options
- •Research summaries
- •Contract review
- •Data interpretation
Coding
Write, explain, debug, and improve code
- •Generate functions
- •Fix bugs
- •Explain code
Important Limitations
Hallucinations
LLMs sometimes make up facts that sound convincing but are completely wrong.
Tip: Always verify important facts from authoritative sources.
No Real-Time Knowledge
LLMs have a training cutoff date. They don't know about events after their training.
Tip: For current events, use models with web access or verify separately.
No True Understanding
LLMs don't "understand" like humans do. They're very sophisticated pattern matchers.
Tip: Treat LLMs as powerful tools, not as oracles or friends.
Context Limits
LLMs can only "remember" a limited amount of conversation. Very long chats may lose early context.
Tip: For long projects, summarize key points periodically.
Key Terms to Know
Parameters
The adjustable settings in a model. More parameters = more capacity to learn patterns.
Token
The basic unit LLMs process. Roughly 4 characters or 0.75 words in English.
Context Window
How much text the model can "see" at once. Larger windows = better at long documents.
Inference
When you use an LLM to generate text. This is what happens when you chat with ChatGPT.
Fine-tuning
Training an existing LLM on specific data to make it better at particular tasks.
Prompt
The input you give to an LLM. The art of writing good prompts is called "prompt engineering."