Prompt

A prompt is the text you send to a language model. Everything the model sees before generating its response is part of the prompt, the system prompt, your message, any prior conversation, any uploaded documents, any tool definitions.

“Prompt engineering” is the discipline of structuring prompts to get reliable, high-quality outputs. In 2026, it’s less mystical than it was in 2023, modern frontier models are forgiving of imperfect prompts, but specific patterns still matter for production use.

Components of a good prompt

• System prompt, sets the model’s role, constraints, voice (“You are a helpful assistant for Australian small business…”)
• Examples (few-shot), show the model 1-3 input/output pairs in the format you want
• Context, the actual data the model needs (documents, prior conversation, query)
• Instruction, what you want done
• Output format, explicit specification of the response shape (markdown, JSON, specific sections)

Patterns that work in 2026

• Chain of thought. Ask the model to think step-by-step. Improves accuracy on complex reasoning.
• Anchoring with examples. Show, don’t just tell. Two well-chosen examples beat 500 words of description.
• Hard rules in caps or explicit lists. “NEVER write to /etc/. ALWAYS confirm before sending email.”
• Negative examples. “Do NOT use phrases like ‘get into’, ‘elevate’, ‘let’s dive in’.”

Patterns that don’t help (or hurt)

• Begging or threatening. “PLEASE just give me a good answer” or “If you fail I’ll lose my job”, doesn’t help on modern models and often makes things worse.
• Repeating instructions. State each rule once. Repetition wastes tokens.
• Over-specification. A 3,000-word prompt for a 200-word task is wasteful. Match prompt length to task complexity.

Australian-specific prompt rules to add

• “Use Australian English (organised, colour, centre, analysed).”
• “Prices in AUD.”
• “Dates in DD/MM/YYYY format.”
• “Do not use em dashes, use en dashes or commas instead.”

Add these to a CLAUDE.md or system prompt once; they apply across every session.

Related terms

• Large Language Model
• Context window
• Prompt caching
• Token