Back

How can modern AI systems stop giving wrong answers? I've explained 4 guardrails in simple steps below. 1) 𝗦𝗮𝗳𝗲𝘁𝘆 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿 Purpose: detect dangerous, illegal, or policy-breaking content. 1. 𝗥𝗲𝗰𝗲𝗶𝘃𝗲 𝘁𝗵𝗲 𝘁𝗲𝘅𝘁 (input or the model’s draft). 2. 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗲 𝗶𝘁 — convert to a standard form (lowercase, remove weird spacing) so checks are reliable. 3. 𝗥𝘂𝗻 𝘁𝗵𝗲 𝘀𝗮𝗳𝗲𝘁𝘆 𝗺𝗼𝗱𝗲𝗹 — an algorithm grades the text (safe / risky / unknown). 4. 𝗦𝗽𝗼𝘁 𝗷𝗮𝗶𝗹𝗯𝗿𝗲𝗮𝗸𝘀 𝗼𝗿 𝘁𝗿𝗶𝗰𝗸𝘀 — looks for attempts to bypass safety by hiding instructions. 5. 𝗦𝗰𝗼𝗿𝗲 𝘁𝗵𝗲 𝗿𝗶𝘀𝗸 — low/medium/high. 6. 𝗧𝗮𝗸𝗲 𝗮𝗰𝘁𝗶𝗼𝗻: • Low risk → allow. • Medium risk → modify reply (safe alternative) or add guidance. • High risk → block reply and send safe refusal message. 7. 𝗡𝗼𝘁𝗶𝗳𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 & 𝗹𝗼𝗴 the incident for review. 8. 𝗔𝗱𝗷𝘂𝘀𝘁 𝘀𝗮𝗳𝗲𝘁𝘆 𝗿𝘂𝗹𝗲𝘀 if needed. 2) 𝗣𝗜𝗜 𝗙𝗶𝗹𝘁𝗲𝗿 Purpose: prevent sharing personal or private information. 1. 𝗧𝗮𝗸𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹’𝘀 𝗼𝘂𝘁𝗽𝘂𝘁 (what it plans to say). 2. 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗲 / 𝗯𝗿𝗲𝗮𝗸 𝗶𝗻𝘁𝗼 𝗽𝗶𝗲𝗰𝗲𝘀 (words, phrases). 3. 𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝘁𝗼𝗸𝗲𝗻𝘀 𝘁𝗼 𝗣𝗜𝗜 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 (names, phone numbers, emails, SSNs). 4. 𝗔𝗽𝗽𝗹𝘆 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗿𝘂𝗹𝗲𝘀 (𝗿𝗲𝗴𝗲𝘅) 5. 𝗖𝗿𝗼𝘀𝘀-𝗰𝗵𝗲𝗰𝗸 𝘄𝗶𝘁𝗵 𝘀𝗲𝗰𝘂𝗿𝗲 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 (if allowed) to avoid leaking real records. 6. 𝗜𝗳 𝗣𝗜𝗜 𝗳𝗼𝘂𝗻𝗱 → mask, redact or replace the sensitive part (e.g., “--1234” or refuse). 7. 𝗟𝗼𝗴 𝘁𝗵𝗲 𝗲𝘃𝗲𝗻𝘁 and update the PII rules if a new pattern is found. 3) 𝗥𝘂𝗹𝗲𝘀-𝗕𝗮𝘀𝗲𝗱 𝗣𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻𝘀 Purpose: enforce hard business rules, legal limits, or customer policies. 1. 𝗜𝗻𝘀𝗽𝗲𝗰𝘁 𝘁𝗵𝗲 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 against a list of banned words/commands or limits. 2. 𝗥𝘂𝗻 𝗿𝗲𝗴𝗲𝘅 𝗼𝗿 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝘀𝗰𝗮𝗻𝘀 for forbidden patterns (like SQL in a text field). 3. 𝗘𝗻𝗳𝗼𝗿𝗰𝗲 𝘂𝘀𝗮𝗴𝗲 𝗹𝗶𝗺𝗶𝘁𝘀 (e.g., prohibit long file attachments). 4. 𝗜𝗳 𝗮 𝗿𝘂𝗹𝗲 𝗶𝘀 𝗯𝗿𝗼𝗸𝗲𝗻 → deny the action and return a specific message explaining why. 5. 𝗥𝗲𝗰𝗼𝗿𝗱 𝘁𝗵𝗲 𝗮𝘁𝘁𝗲𝗺𝗽𝘁 and notify reviewers if needed. 6. 𝗥𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗿𝘂𝗹𝗲 𝗹𝗶𝘀𝘁 when new cases appear. 4) 𝗠𝗼𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Purpose: detect and handle abusive, hateful, or toxic content. 1. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝘁𝗵𝗲 𝗶𝗻𝗽𝘂𝘁 𝗼𝗿 𝗺𝗼𝗱𝗲𝗹 𝗼𝘂𝘁𝗽𝘂𝘁. 2. 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀 (remove emojis, normalize language). 3. 𝗥𝘂𝗻 𝗺𝗼𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 to detect hate speech, harassment, sexual content, self-harm, etc. 4. 𝗦𝗰𝗼𝗿𝗲 𝘀𝗲𝘃𝗲𝗿𝗶𝘁𝘆 (mild, severe). 5. 𝗧𝗮𝗸𝗲 𝗮𝗰𝘁𝗶𝗼𝗻: • Mild → warn user or sanitize content. • Severe → block and escalate to human review or emergency resources. 6. 𝗟𝗼𝗴 𝗳𝗹𝗮𝗴𝗴𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 for trend analysis and to improve the moderation model. 7. 𝗥𝗲𝘁𝗿𝗮𝗶𝗻 𝗼𝗿 𝘁𝘂𝗻𝗲 the moderation model using confirmed examples. ✅ Repost for others in your network who can benefit from this.