Large language models are not magic — they're pattern matchers trained on vast text corpora. Understanding this changes how you build with them.
The basics
An LLM takes a sequence of tokens (pieces of text) and predicts the next token. Stack enough predictions and you get coherent responses.
Key concepts
- Context window: How much text the model can see at once
- Temperature: Controls randomness in outputs
- System prompts: Instructions that shape model behavior
When to use LLMs
Use them for tasks involving language understanding, generation, and reasoning over text. Don't use them when you need deterministic computation or real-time data without tools.
Next steps
Start with a simple API call, evaluate outputs critically, and iterate on your prompts.