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Meilisearch logoMeilisearch · Feature trail

RAG Guardrails

1 post · May 2026

What this trail covers

Meilisearch Chat has been enhanced with RAG guardrails to ensure trustworthy AI applications. This includes structured controls for validating user input and constraining LLM outputs to prevent hallucinations, data leakage, and prompt injection. Guardrails are implemented across the RAG pipeline: pre-retrieval (query validation, prompt injection detection), during retrieval (data source management, metadata filtering), and post-generation (response accuracy, compliance). The post details tools like Guardrails AI, NVIDIA NeMo Guardrails, and integrations with LangChain or OpenAI APIs, and emphasizes the importance of search quality for effective guardrails. Meilisearch supports these guardrails through its fast hybrid search, structured filtering, metadata filtering for access controls, and schema-aware indexing for document consistency.

All Meilisearch trails
  1. RAG guardrails: the foundation of trustworthy AI applicationsThis post details the implementation and importance of RAG guardrails, focusing on their role in validating user input and constraining LLM outputs to prevent hallucinations, data leakage, and prompt injection. It outlines how guardrails operate at three critical points in the RAG pipeline: before retrieval (query validation, prompt injection detection), during retrieval (data source management, metadata filtering), and after generation (response accuracy, compliance). The post also discusses common RAG guardrail metrics, best practices, mistakes to avoid, and the direct impact of search quality on guardrail effectiveness. It highlights Meilisearch's contribution to RAG guardrails through its hybrid search, structured filtering, metadata filtering, and schema-aware indexing.