Prompt Engineering

Prompt engineering is the practice of shaping a model's input to get better, more reliable output. Because an LLM is a pattern-completion engine, the context you provide steers the statistical distribution of what it produces — clearer instructions, useful examples, and the right framing measurably improve results.

Core techniques include being specific about the desired format, giving a few worked examples (few-shot prompting), asking the model to reason step by step (chain-of-thought), and assigning it a role. None of these change the model; they all change the input.

As models improve, some hand-tuned prompt tricks matter less — but the underlying skill, clearly communicating intent and constraints, only becomes more valuable.