Advanced RAG: complete definition in AI for SMEs

Advanced RAG refers to techniques that improve a basic RAG system's quality beyond simple "search then inject." It includes: hybrid search (keywords + meaning combined), reranking (ordering results by relevance after initial retrieval), query rewriting (reformulating the user's question to improve search) and iterative RAG (chaining multiple successive searches).

What it changes for an SME

Basic RAG works for simple cases. Advanced RAG is needed when:

• documents are long and the exact answer is buried in text (reranking);
• the user's question is ambiguous and must be reformulated (query rewriting);
• the answer requires cross-referencing multiple sources (iterative RAG).

Concretely, advanced RAG moves accuracy from 70% to over 90% — the difference between an approximate assistant and a reliable one.

The trap

Over-complexifying too quickly. You always start with simple RAG, measure, and add an advanced technique only where simple falls short. Each technique adds complexity and cost. In fractional AI leadership, we iterate: simple RAG → measure → hybrid search → measure → reranking if needed.