You don't need a PhD to understand how language models work. Here's the mental model that will make you a better prompt engineer.
Large language models (LLMs) like GPT-4, Claude, and Gemini are trained on massive amounts of text. They learn patterns — how words relate to each other, how sentences flow, what typically follows what.
When you give them a prompt, they're essentially predicting: "What text would most likely come next?"
Understanding this changes how you write prompts:
1. Context is everything
The model uses your entire prompt as context for its prediction. More relevant context = better predictions = better output.
2. The model follows patterns
If you start with a formal tone, it continues formally. If you provide examples in a specific format, it follows that format. This is called "few-shot prompting" and we'll cover it later.
3. It doesn't "know" things — it predicts
The model isn't looking up facts in a database. It's generating the most likely response based on patterns. This is why it can sometimes be confidently wrong (hallucination).
Most AI tools have a "temperature" setting:
The better you set up the context and pattern for the model to follow, the better your results will be. That's prompt engineering in a nutshell.