RAG guardrails: the foundation of trustworthy AI applications

This 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.