Guardrails for LLM Apps: Input/Output Filtering Patterns (ANSWERED)
Scenario question on LLM guardrails — prompt injection defense, output validation, NeMo/Guardrails patterns, and policy layers.
TL;DR — Quick Answer
Guardrails wrap the LLM with deterministic and ML checks. Input: injection detection, PII scrubbing, topic allowlists, max length, canonicalization. Output: schema validation, toxicity/PII scanners, citation verification, regex for secrets, refusal templates, human escalation triggers. Layer defenses — don't rely on system prompt alone. Log blocked events; tune false positive rate with human review; separate safety policy from business logic validators.
The Interview Question
Design guardrails for an LLM application. What input and output filtering patterns do you use in production?
Deep Explanation
Sign in to unlock full answer
Get deep explanations, PDF export & all LLMs questions
- 12 more sections of deep explanation
- Real-world examples
- Common mistakes
- Interviewer expectations
- Follow-up questions
GuardrailsSafetyPrompt InjectionValidationProductionAnthropicMicrosoftOpenAI