Why prompts matter
The same language model can produce wildly different results depending on how you ask. A vague prompt like "write about marketing" yields generic filler. A specific prompt with audience, tone, length, and structure constraints yields content you can actually use.
Prompt engineering is less about secret tricks and more about clear communication. You're briefing a capable but literal assistant — the more context you provide, the better the output.
Key techniques
System prompts set the model's role, tone, and boundaries before any conversation begins. "You are a customer support agent for a UK plumbing company. Be concise and professional. Never recommend competitors."
Few-shot examples show the model what good output looks like by including input-output pairs in the prompt. This is especially effective for formatting structured data or matching a brand voice.
Chain-of-thought asks the model to reason step by step before answering, which improves accuracy on complex questions. "Think through the customer's issue, then recommend the most relevant service."
Prompts in production
When building automated workflows, prompts become part of your codebase — versioned, tested, and maintained like any other business logic. We treat prompt development as engineering, not guesswork, and iterate based on real output quality.