What is prompt engineering?

Prompt engineering is the practice of crafting inputs to large language models that consistently produce useful outputs. It's the difference between "write me an email" (vague, generic result) and a structured prompt that specifies audience, tone, length, format, and constraints (precise, usable result).

Core techniques: 1) System prompts — define the AI's role, constraints, and output format before user input. 2) Few-shot examples — include 2-3 examples of ideal input/output pairs in the prompt. 3) Chain of thought — ask the model to "think step by step" for complex reasoning tasks. 4) Output formatting — specify JSON, markdown, bullet points, or other structured formats. 5) Constraints — set explicit boundaries ("respond in under 100 words", "use only information from the provided context").

Common mistakes: prompts that are too vague ("help me with marketing"), too long (burying the actual instruction in context), or contradictory (asking for both brevity and comprehensiveness). The best prompts are specific about what they want and explicit about what they don't.

For production systems: prompt engineering is 60% of the work in shipping AI features. The model is commodity — the prompt is the product. Version control your prompts like code. A/B test prompt variations. Measure output quality systematically, not anecdotally.

The skill compounds: experienced prompt engineers get 3-5× better results from the same model. It's worth investing in, especially for teams building AI products.