RAG indexing: Structure and evaluate for grounded LLM answers

What this post added

This post details the process of RAG indexing, including data collection, chunking, metadata addition, embedding, and storage in a vector index, to improve AI grounding and relevance. It explores various indexing strategies like hierarchical, hybrid, time-based, multi-representation, domain-specific, and graph-enhanced indexing, and discusses common challenges and best practices for RAG indexing pipelines, highlighting Meilisearch's support for vector and hybrid search.