Prompt Engineering
Designing the text instructions given to LLMs to produce reliable outputs.
Prompt engineering is the practice of designing input text — instructions, context, examples — that reliably produces desired outputs from a Large Language Model. Effective prompts specify task, constraints, format, and examples in ways that guide the model toward consistent behavior.
Context
Prompt engineering is becoming less of a specialized skill and more of a normal software-development practice. The model becoming more capable over successive generations reduces the sensitivity to prompt phrasing, but structured prompts still outperform casual ones.
Key techniques: explicit role definition ('You are an experienced copywriter…'), chain-of-thought prompting ('Think step by step before answering'), few-shot examples (showing the model 2–5 input/output pairs), and structured output requests (return JSON, return markdown, etc.).
A prompt for ad copy generation that consistently outperforms generic requests: '[Role] You are an experienced DTC copywriter. [Context] Product: [X]. Target audience: [Y]. Past winning ads have used curiosity hooks, not outcome hooks. [Task] Generate 10 Facebook primary-text variations. [Format] Return as a numbered list with one line per variant, under 125 characters each.'
Long elaborate prompts sometimes perform worse than short ones because the model's attention diffuses across too many instructions. Iterating prompt length is as important as iterating wording.