What this prompt does
This prompt designs an output-validation layer that sits between an LLM and your users, wrapping every model call as middleware in [language]. For a given [app_type], it specifies PII detection and redaction for [pii_types], content-policy blocking of [blocked_content], schema validation against [output_schema], hallucination detection via [detection_method], toxicity and bias scoring, brand-safety checks against [brand_guidelines], prompt-leakage detection, length and cost guardrails tied to [max_tokens], logging of every trigger, and graceful fallback responses when output is blocked.
The structure works because it treats safety as a pipeline of independent checks rather than one fuzzy filter. Each variable maps to a concrete gate: [pii_types] defines what gets redacted, [output_schema] defines what valid structure looks like, and [max_tokens] caps runaway generations before they cost you. Implementing it as middleware means it wraps any LLM API call without rewriting your application logic, and the logging requirement is what turns one-off blocks into data for ongoing model improvement. The [brand_guidelines] and [blocked_content] variables let you encode policy once and apply it to every response, so safety becomes a consistent layer rather than scattered ad-hoc checks.
When to use it
- You are shipping a customer-facing AI assistant and need outputs validated before display
- You must redact PII like emails, phone numbers, or card data from responses
- You need to guarantee outputs match a strict JSON schema before downstream code consumes them
- You want to block specific content categories such as medical, legal, or financial guarantees
- You need an audit log of every guardrail trigger for review
- You want fallback responses instead of raw failures when output is blocked
Example output
Expect a middleware design plus implementation scaffolding in [language] that wraps an LLM call, runs each check in sequence, and either returns the validated output or a fallback. You will see redaction logic for [pii_types], schema validation against [output_schema], a logging structure for triggers, and a description of a monitoring dashboard tracking guardrail hit rates. It reads as a layered pipeline you can drop in front of existing calls, with prompt-leakage detection and graceful fallback responses included so blocked outputs degrade politely instead of erroring.
Pro tips
- List
[pii_types]precisely; "email, phone, SSN, credit card, address" yields tighter redaction than a vague "personal data" - Make
[output_schema]strict and explicit so format validation can actually reject malformed responses - Set
[max_tokens]to a real budget so the length guardrail truncates or regenerates instead of letting cost spiral - Be concrete in
[blocked_content]— naming categories like medical or legal advice prevents over-blocking benign text - Treat
[detection_method]honestly; cross-referencing retrieved sources plus a lightweight judge reduces but never eliminates hallucinations - Keep the trigger logs and review them; that feedback loop is what improves the guardrails over time
