Blogs›Meilisearch›RAG Guardrails
This post introduces RAG guardrails as a foundational element for trustworthy AI applications. It details what RAG guardrails are, why they are necessary to mitigate risks like hallucinations, data leakage, and prompt injection, and how they function across the RAG pipeline (pre-retrieval, during retrieval, and post-generation). The post also discusses tools that support guardrails (Guardrails AI, NVIDIA NeMo Guardrails, LangChain, OpenAI APIs), common metrics for measuring their effectiveness, best practices for implementation, and common mistakes to avoid. It highlights how Meilisearch's hybrid search and structured filtering capabilities contribute to robust RAG guardrails by ensuring contextual grounding and controlled data access.
5/21/2026
Introduces the concept of RAG guardrails, detailing their necessity, operational stages (pre-retrieval, during retrieval, post-generation), supporting tools, metrics, best practices, and common mistakes. It specifically calls out Meilisearch's role in supporting RAG guardrails through its hybrid search, structured filtering, metadata filtering, and schema-aware indexing for improved contextual grounding and data control.