RAG & Retrieval
Grounding AI answers in your own documents and data.
RAG & Retrieval
Vector databases explained: what they do and when you need one
A vector database stores embeddings and finds the nearest ones fast with an approximate index. Below a few million vectors, your current database may be enough.
RAG & RetrievalChunking strategies for RAG: size, overlap and structure
Good RAG chunking splits documents on their own structure, keeps chunks a few hundred tokens with context, and tests size and overlap on real questions.
RAG & RetrievalRAG explained: why AI answers go wrong and how retrieval fixes it
RAG makes AI answer from your own documents by retrieving relevant passages first; most failures come from poor chunking, search, freshness or access rules.