Large Language Model (LLM)
A Large Language Model is an AI system trained on massive text datasets to understand and generate human language. LLMs power tools like ChatGPT, Claude, and Cursor, enabling them to write code, answer questions, and engage in natural conversation by predicting probable sequences of words.
Example
When you ask Claude to write a React component, you're using an LLM that has learned patterns from billions of lines of code and can generate new code that follows those patterns.
Large Language Models are the technology that makes vibe coding possible. Understanding how they work helps you use them more effectively.
What Makes an LLM "Large"
- Parameters — Billions of learned values (GPT-4 has over 1 trillion)
- Training data — Trillions of tokens from books, websites, code
- Compute — Thousands of GPUs running for months
This scale enables emergent capabilities — abilities that smaller models don't have.
How LLMs Generate Code
- Tokenization — Your prompt is split into tokens
- Context processing — Model reads and understands the full context
- Next-token prediction — Model predicts the most likely next token
- Iteration — Repeat until response is complete
The model doesn't "think" — it predicts probable continuations based on training patterns.
Popular LLMs for Coding
| Model | Provider | Strengths |
|---|---|---|
| GPT-4o | OpenAI | Fast, good coding |
| Claude 3.5 | Anthropic | Long context, reasoning |
| Gemini | Multimodal, huge context | |
| Llama 3 | Meta | Open source |
Why LLMs Are Good at Code
- Code is structured and rule-based
- Massive amounts of open-source code in training data
- Comments provide natural language → code mappings
- Programming patterns are predictable
Limitations to Remember
- Knowledge cutoff (not real-time)
- Can hallucinate APIs
- No actual code execution
- Reasoning can fail on novel problems
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