Guardrails
Programmatic checks around LLM inputs and outputs — what the model can be asked, what it is allowed to return.
What is Guardrails?
Guardrails are the deterministic safety layer around a probabilistic LLM. They validate inputs (block prompt injection, PII leaks, off-topic queries) and outputs (block hallucinated SQL, harmful content, format violations) — typically using rules, classifiers, or smaller LLMs as judges.
Common guardrail categories: **input validation** (length, PII, prompt-injection patterns), **content moderation** (toxicity, self-harm), **factuality** (does the answer cite retrieved sources?), **format** (is the JSON valid?), **policy** (does this response follow company-specific rules?).
Libraries: NeMo Guardrails (NVIDIA), Guardrails AI, LlamaGuard. Or implement custom: a fast classifier model, a rule engine, an LLM-as-judge that scores outputs. Production agents nearly always have several layers.
Guardrails are the difference between a fun demo and a system you can ship to customers. Indian enterprises increasingly demand them in RFPs.
A Mumbai healthcare chatbot has 4 guardrails: a PII redactor on input, a topic classifier (block non-health), a citation enforcer (refuse claims without sourced evidence), and an emergency-handoff trigger ("if user describes chest pain, escalate to human within 10 seconds").
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