What is an LLM?
As a trainer, you need to clearly explain what an LLM is at its core—and just as importantly, what it is not!
The Core Concept
A Large Language Model (LLM) is, at its core, just a next-word prediction machine. That's it!
Think of it like an extremely sophisticated autocomplete:
- You type some text
- It predicts what word is likely to come next
- It adds that word to your text
- Repeat
What an LLM Is NOT
An LLM is not:
- Conscious or self-aware
- Actually "thinking" or "reasoning"
- Understanding meaning like humans do
- Storing facts like a database
It's just really good at pattern matching and predicting what text typically follows other text.
Key Points to Emphasize
1. It's All About Probability
- For any input text, the LLM calculates probabilities for every possible next word
- It then (usually) picks the most likely one
- Sometimes it adds randomness to be more creative.
- This randomness is controlled by a parameter called "temperature." A higher temperature makes the model's predictions more random and creative, while a lower temperature makes them more focused and deterministic.
2. Context Matters
- LLMs look at the recent text (the "context window") to make predictions
- They don't remember conversations beyond their context window
- Each prediction only considers what's in this window
3. Training Data is Key
- LLMs learn patterns from their training data
- They can only predict patterns they've seen before
- They don't learn from conversations after training
4. Hallucinations are wrong predictions
- LLMs sometimes make up information that isn't in their training data
- This is called a "hallucination"
- Hallucinations are basically when the LLM get its predictions wrong!
Why This Matters
Understanding LLMs as next-word predictors helps:
- Set realistic expectations
- Write better prompts
- Understand limitations
- Avoid anthropomorphizing
Remember: Despite their impressive outputs, LLMs are fundamentally pattern matching machines, not thinking beings.