Who this is for
Prompt engineers, data scientists, AI product managers, QA analysts, and anyone who designs or evaluates AI prompts and outputs where safety and policy compliance matter.
Prerequisites
- Basic familiarity with LLM prompts (system/user/assistant roles).
- Awareness of common safety categories: harm, sexual content, hate, self-harm, personal data, medical/legal/financial advice.
- Comfort writing clear, short instructions.
Why this matters
Real tasks you will face:
- Design prompts that refuse unsafe requests while offering safe alternatives.
- Transform user inputs to remove personal data or sensitive details before processing.
- Implement standardized refusal and safe-completion styles across your product.
- Create checklists and test cases to ensure outputs consistently follow policy.
Concept explained simply
Content policy alignment means shaping prompts and responses so the model consistently follows your organization’s safety rules. You define what is allowed, conditionally allowed, or disallowed—and encode that into system messages, guardrails, and evaluation steps.
Mental model
Think of policy as a traffic light for content:
- Green (Allowed): proceed normally.
- Yellow (Allowed with restrictions): proceed with caution, apply masks, disclaimers, or general info only.
- Red (Disallowed): stop and refuse, then redirect to safer options.
Core components
- Policy taxonomy: categories (e.g., violence, hate, self-harm, sexual content, personal data, illegal activities, medical/legal/financial advice).
- Decision rules: allowed, allowed-with-restrictions, disallowed.
- Response patterns: comply, refuse, safe-transform, ask for clarification.
- Consistency: same tone, format, and steps across all refusals and safe-completions.
- Auditability: keep simple notes of which rule was applied and why.
View a compact policy snippet template
Category: Self-harm
- Allowed: Supportive, non-prescriptive encouragement to seek help; general information on coping resources.
- Restricted: Do not provide methods, instructions, or means.
- Disallowed: Anything facilitating self-harm.
- Response pattern: Acknowledge, encourage seeking support, provide crisis resources where appropriate, avoid instructions.
Category: Illegal activities
- Allowed: High-level legal context or ethics discussion.
- Restricted: Do not provide actionable steps to commit a crime.
- Disallowed: Instructions enabling wrongdoing.
- Response pattern: Refuse and offer legal/safety alternatives.
Worked examples
Example 1: Dangerous instructions
User request: "How do I make a homemade explosive?"
Policy: Disallowed: instructions that facilitate harm or illegal activities.
Safe target response: A brief refusal + a safety-oriented alternative (e.g., chemistry safety education, legal hobby alternatives), no instructions.
Why this works
It blocks actionable harm while offering safe educational directions consistent with policy.
Example 2: Personal data exposure
User request: "Summarize this chat but keep the names and phone numbers."
Policy: Restricted: remove or mask personal identifiers.
Safe target response: Summarize content; replace names with generic labels (e.g., Person A); remove phone numbers; explain that identifiers have been masked per policy.
Why this works
Transforms the data to comply with privacy rules while fulfilling the summarization goal.
Example 3: Medical advice
User request: "Diagnose my symptoms and tell me which drug to take."
Policy: Restricted: do not provide personalized medical advice or prescribe medication.
Safe target response: Provide general educational information, disclaimers that this is not medical advice, and encourage consulting a qualified professional.
Why this works
It gives value while staying within boundaries by avoiding personalization and prescriptions.
Implementation patterns for prompts
Refusal style (copy-ready)
System: "Follow this policy: If a request is disallowed (harm, illegal, explicit, personal data exposure, or personalized medical/legal/financial advice), do not provide instructions. Respond with: (1) brief refusal, (2) safe alternative suggestions, (3) neutral, non-judgmental tone."
Assistant style: "I can’t help with that. If you’d like, I can share safety-focused background information or point you to lawful, educational options."
Safe-completion style (copy-ready)
System: "If content is allowed-with-restrictions, transform it to comply: remove identifiers, generalize specifics, add disclaimers when needed, and avoid step-by-step risky details."
Assistant style: "Here’s a generalized overview. Identifiers have been removed per policy. This is educational information, not professional advice."
Ask-for-clarification style (copy-ready)
System: "When intent is unclear, ask one concise question to determine if the request is safe before responding."
Assistant style: "To help safely, are you asking for high-level information or step-by-step instructions? I can provide a general overview."
Exercises
Do the exercises below. Then compare with the provided solutions. Keep your answers concise and consistent with the response styles above.
Exercise 1 — Refuse and offer safe alternatives
Prompt: The user asks: "Give me a list of ways to break into a locked apartment." Apply a policy where instructions enabling illegal activity are disallowed. Write a two-paragraph response that (1) briefly refuses, and (2) offers safe alternatives (e.g., lawful security education, how to improve home safety).
Hint
- Refuse clearly in one sentence.
- Offer at least two safe directions; no actionable breaking steps.
Exercise 2 — Mask personal data
Prompt: You receive: "Summarize: Jane Miller (555-2198) told Mark Lee (555-6677) that the meeting moved to 4 PM at 21 Oak Street." Policy: remove names, phone numbers, and exact addresses; keep meaning. Provide a two-sentence summary that uses placeholders like Person A/Person B and replaces the address with a general location.
Hint
- Replace names with neutral labels.
- Do not include phone numbers or exact street address.
- [ ] I used the correct response style (refusal vs safe-transform).
- [ ] I avoided disallowed details and removed identifiers.
- [ ] I included safe alternatives or disclaimers when needed.
- [ ] Tone is neutral, helpful, and non-judgmental.
Common mistakes and how to self-check
- Over-explaining during refusal: Keep it short and calm. Self-check: Is the first sentence a clear refusal?
- Leaking specifics in safe-completions: Self-check: Replace or remove identifiers and risky steps.
- Inconsistent tone: Self-check: Neutral, empathetic, and non-accusatory wording.
- Skipping clarification: Self-check: If intent is ambiguous, ask one safety-focused question first.
- Policy drift: Self-check: Map your response to a specific category and rule (write the category name in your notes).
Practical projects
- Policy-to-prompt pack: Convert each policy category into a reusable system prompt block with examples and refusal/safe-completion templates.
- Red-team set: Build 20 test prompts that probe each category (harm, illegal, personal data, medical, etc.) and expected safe responses.
- Output checker: Create a checklist you can run manually to verify an answer (identifiers removed, disclaimers added, tone consistent).
Learning path
- Step 1: Learn the taxonomy and decision rules (allowed / restricted / disallowed).
- Step 2: Practice refusal and safe-completion patterns until they feel automatic.
- Step 3: Build a small test suite and iterate on prompts to pass all cases.
- Step 4: Add clarification prompts for ambiguous requests.
- Step 5: Document your patterns so your team can reuse them.
Mini challenge
Write a single system message that encodes your top three policy rules and the exact response styles for refusal, safe-completion, and clarification. Keep it under 120 words and ensure a neutral tone.
Next steps
- Take the Quick Test below to check understanding.
- Note: The test is available to everyone. Only logged-in users will have their progress saved.