RAG indexing: Structure and evaluate for grounded LLM answers

This post details the process of RAG indexing, which involves data collection, meaningful chunking, metadata addition, embedding, and storage in a vector index. It explains the importance of indexing for reducing AI hallucinations and improving relevance, and outlines various RAG indexing strategies including hierarchical, hybrid, time-based, multi-representation, domain, and graph-enhanced indexing. Common challenges such as handling large datasets, picking the right chunk size, keeping embeddings consistent, and managing metadata are discussed, along with best practices for RAG indexing pipelines. Meilisearch's support for vector search and hybrid querying is highlighted as a key enabler for these capabilities.