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Hybrid RAG in practice — identifiers meet paraphrases

· 2 min read
Qefro Team
Qefro Team
Qefro

Hybrid RAG combines lexical (keyword-oriented) retrieval with vector (semantic) retrieval so assistants can find both exact identifiers and paraphrased questions — then ground answers with citations.

Why vectors alone struggle in enterprise corpora​

Customer and employee knowledge bases are full of:

  • SKUs and part numbers
  • Error codes (E-4421)
  • Policy IDs and ticket prefixes
  • Product names that look like noise to an embedding model

Pure semantic search often misses those. Pure keyword search misses “how do I get my money back?” when the doc title is “Refund policy.” Hybrid retrieval covers both.

How it fits the Knowledge Platform​

Hybrid RAG is the retrieval engine. The AI Knowledge Platform wraps it with ingest, OCR, workspace isolation, and ops.

Question
  → lexical retriever
  → vector retriever
  → fusion / re-rank
  → grounded generation + citations

Concept deep dive: Hybrid RAG. Product: Knowledge Platform.

Quality loop we recommend​

  1. Curate sources — remove duplicate drafts and contradictory wikis.
  2. Test identifier queries — SKUs, error codes, policy numbers.
  3. Test paraphrase queries — natural variants of the same intent.
  4. Inspect citations — wrong citation means fix chunking or source, not only the system prompt.
  5. Refuse when empty — prefer an honest gap over inventing policy.

Isolation still comes first​

Hybrid RAG runs inside a workspace index. Tenancy ensures one organization’s documents never appear in another’s answers. See Multi-tenant AI Architecture.

When live state is required (order status, account balance), pair RAG with Business Actions — docs for policy, APIs for state.