Advanced RAG — Query Routing, Reranking & Self-RAG

I will implement advanced RAG techniques beyond basic similarity retrieval — covering query routing to direct different query types to appropriate retrieval strategies, a cross-encoder reranker improving precision of retrieved chunks, self-RAG for relevance self-assessment, and corrective RAG that triggers web search when the knowledge base lacks sufficient information. Advanced RAG techniques each address specific failure modes of basic RAG — reranking improves precision for queries where semantic similarity retrieves topically related but not precisely relevant chunks; query routing handles queries that require structured data retrieval or calculation rather than semantic knowledge retrieval; corrective RAG prevents confident answers from insufficient knowledge.

The implementation covers query classifier for routing, strategy-specific retrieval per query type, cross-encoder reranking, self-assessment for retrieval sufficiency, corrective retrieval fallback, and quality improvement comparison against your basic RAG baseline.