Learn vocabulary for structuring prompts: role prompting, context setting, output format specification, system vs user messages, and constraints.
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What is 'role prompting' in the context of language model prompts?
Role prompting means instructing the model to adopt a specific persona or area of expertise — for example, 'Act as a senior DevOps engineer with experience in Kubernetes.' This frames the tone and knowledge level of the response.
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What is a 'system message' in a prompt, versus a 'user message'?
In most LLM APIs, the system message is a special role used to configure the model's behaviour across the conversation — defining persona, constraints, and output style. The user message is the actual request each turn.
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What does 'output format specification' mean in prompt engineering?
Output format specification means telling the model exactly what structure you want — JSON, Markdown, numbered list, table, etc. Without this, models often vary their format unpredictably across requests.
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What is a 'constraint' in a prompt, and why is it useful?
Constraints bound the model's response to meet specific requirements — length limits, format restrictions, topic scope, or exclusions. They help make outputs more predictable and fit for purpose.
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What does 'context setting' mean at the start of a prompt?
Context setting means front-loading relevant background into the prompt so the model can produce a more accurate, tailored response — for example, 'We have a React app using TypeScript and Redux. The following component has a performance issue...' before asking for help.