RAG guardrails are structured controls embedded in retrieval-augmented generation pipelines to validate inputs, constrain LLM outputs, and prevent hallucinations, data leakage, and prompt injection attacks. They operate at three stages: before retrieval (query validation, intent filtering), during retrieval (metadata filters, similarity thresholds, audit logs), and after generation (output validation, PII redaction, policy enforcement). Key tools include Guardrails AI, NVIDIA NeMo Guardrails, LangChain, and OpenAI moderation APIs. Best practices include embedding guardrails across the full pipeline, using strict role-based access controls, running red-team tests, and maintaining dataset hygiene. Common mistakes include over/under-filtering, weak monitoring, and missing refusal logic. Meilisearch is presented as a retrieval layer that strengthens guardrails through hybrid search, metadata filtering, and schema-aware indexing.
