Prompt Engineering

Prompt engineering is the iterative practice of writing and testing prompts so a language model behaves consistently for a given task. It covers instructions, examples, output formats, and how context is arranged.

Why it matters

A well-built prompt can replace more expensive options like fine-tuning and makes outputs easier to parse and validate. Because models change between versions, prompts used in production need regression tests.

Example

Common techniques include giving a role, supplying examples (few-shot prompting), and demanding a structured format:

Classify the ticket as "billing", "bug", or "other".
Return JSON: {"category": "...", "reason": "..."}

Ticket: "I was charged twice this month."

Asking for JSON lets your code validate the answer and retry when it is malformed.

Common mistakes

• Judging a prompt from a single try instead of a set of varied test cases.
• Making prompts long and contradictory rather than clear and minimal.
• Trusting the output without validation, since the model can still hallucinate.

Related

• Prompt
• Context window
• Retrieval-augmented generation
• JSON formatter

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