Guardrails
Safety constraints and validation mechanisms that prevent AI systems from producing harmful, incorrect, or policy-violating outputs.
Guardrails are protective mechanisms that constrain AI behavior to ensure safety, correctness, and policy compliance. They act as boundaries around what an AI system can input, process, and output—preventing harmful content, privacy leaks, hallucinations, and unauthorized actions.
Guardrails operate at multiple layers: Input validation (filtering malicious prompts, PII, jailbreak attempts), Processing constraints (rate limiting, cost caps, timeout enforcement), and Output validation (content moderation, schema enforcement, fact-checking). Effective guardrails combine all three layers to create defense in depth.
Input guardrails detect and block problematic user inputs before they reach the model. Examples include: prompt injection detection (identifying attempts to override instructions), PII scrubbing (removing social security numbers, credit cards), topic filtering (blocking requests for illegal content), and context limiting (preventing excessively long inputs that waste tokens).
Output guardrails validate model responses before showing them to users. Techniques include: Schema validation (ensure JSON matches expected structure), Content moderation (detect hate speech, violence, sexual content using classifiers), Fact verification (cross-check claims against knowledge bases), Consistency checking (detect contradictions within responses), and Grounding verification (ensure RAG responses cite actual retrieved content).
Processing guardrails prevent resource abuse and runaway costs. Examples: Rate limiting (max requests per user/minute), Token budgets (cap context window usage), Iteration limits (prevent infinite agentic loops), Tool allowlists (restrict which functions agents can call), and Human-in-the-loop (require approval for high-risk actions like sending emails or financial transactions).
Implementation approaches vary: Libraries (Guardrails AI, NeMo Guardrails, LlamaGuard), Prompt engineering (system prompts with rules), Fine-tuned classifiers (train models to detect violations), Rule-based filters (regex, keyword blocking), and LLM-as-judge (use another model to evaluate outputs). Production systems typically combine multiple approaches.
Use guardrails when: (1) AI outputs face users or external systems, (2) privacy/compliance is required, (3) agents have tool access, or (4) costs must be controlled. Avoid over-constraining: guardrails that are too strict reduce utility. The goal is to prevent genuine harms while allowing legitimate use cases.
Key Takeaways
- Guardrails are safety constraints that prevent harmful, incorrect, or policy-violating AI outputs
- Operate at three layers: input validation, processing constraints, and output validation
- Input guardrails block prompt injection, PII, and malicious requests before reaching the model
- Output guardrails validate responses via schema checking, content moderation, and fact verification
- Processing guardrails prevent resource abuse through rate limiting, token budgets, and iteration caps
- Implementation uses libraries (Guardrails AI, NeMo), classifiers, rules, or LLM-as-judge approaches
- Critical for production systems, especially those with tool access or user-facing outputs
In This Platform
This platform implements several guardrails: JSON schema validation ensures all content matches expected structure, source reference validation prevents unsupported claims, cross-reference checking catches broken links between concepts, and the build process fails if critical validations don't pass—demonstrating validation at build time rather than runtime.
- build.js
- schema/survey.schema.json
- schema/concept.schema.json