Why hybrid RAG matters
Foundation models are strong reasoners but weak custodians of your private, evolving context. Hybrid RAG combines retrieval channels so each query gets the right balance of breadth and precision.
A practical architecture
Use a retrieval stack with:
- dense retrieval for semantic coverage
- sparse or keyword retrieval for exact token matches
- graph traversal for relationship-sensitive questions
Then perform late fusion ranking to compose a grounded evidence set before generation.
Prompting strategy
Prompts should enforce evidence usage and uncertainty behavior:
- cite source context IDs
- state when evidence is insufficient
- avoid speculative synthesis outside retrieved context
Evaluation metrics that matter
Track more than answer quality:
- citation faithfulness
- unsupported claim rate
- retrieval recall for critical entities
- response latency by query class
These metrics surface safety and reliability issues much earlier than human QA alone.