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Applied AI / Forward Deployed Engineer · Prompt Engineering · Lesson 1 of 4

Anatomy of a production prompt

7 min

Amateurs write a sentence. Professionals write a structured prompt with clear roles and sections. A strong production prompt usually has:

  • Role / system prompt: who the model is and its high-level goal + constraints.
  • Task instructions: precise, unambiguous steps. Tell it what to do and what not to do.
  • Context: retrieved documents, user data, examples — clearly delimited.
  • Output format: exact shape (JSON schema, sections). Show, don't just tell.
  • Guardrails: how to handle unknowns ("If the answer isn't in the context, say 'I don't know'").
SYSTEM: You are a support-ticket classifier for an e-commerce company.
Classify each ticket into exactly one category and rate urgency.

Rules:
- Use ONLY these categories: [billing, shipping, returns, technical, other]
- If unsure, use "other". Never invent a category.
- Output valid JSON only, no prose.

Output schema:
{ "category": string, "urgency": "low"|"medium"|"high", "reason": string }

Ticket: """{ticket_text}"""
Delimiters matterWrap user/injected content in clear delimiters (triple quotes, XML tags like <document>). It reduces confusion and is your first line of defense against prompt injection.

◆ Lock it in

  • Structure a prompt into role, instructions, context, output format, guardrails.
  • State the negative space: what NOT to do, and how to handle unknowns.
  • Delimit injected content clearly — clarity + injection defense.
Feynman drill — say it out loudList the five sections of a robust production prompt from memory.
Step 1 rate your confidence · Step 2 pick your answer
Which addition most improves reliability of a classification prompt?
Step 1 — how sure are you?