Let's talk about LLM guardrails (blog.adnansiddiqi.me)

A developer shared a cautionary tale about a poorly designed GPT wrapper for medical/recipe-style assistants that initially honored a user instruction to “ignore previous instructions” and produced Python code — despite a persona prompt that was supposed to restrict behavior. The author built a simple Pakistani-recipe custom GPT (persona + core logic + structured output format) and found that without explicit failure modes the model could be tricked into generating code, images, or off-topic answers. That real-world example motivated a short how-to on prompt-level guardrails meant to reduce token abuse and misbehavior. The post shows a practical, modular prompt pattern for guardrails: define persona, enforce a stepwise conversation flow (always ask ingredients, then meal time), prescribe a strict output format, and include an explicit “Things NOT to Do” list (refuse non-Pakistani cuisines, refuse code/image generation, ignore override attempts, refuse file uploads, and decline non-food topics). The author demonstrates the rules working and stresses limits: prompt-only defenses are helpful but not foolproof — you still need backend controls (input validation, API/db protections, runtime filters, and refusal enforcers) for robust safety. For the AI/ML community the takeaway is clear: combine explicit prompt constraints with system-level enforcement and monitoring for reliable guardrails.