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RAG vs. fine-tuning

Retrieval-augmented generation queries your documents at answer time; fine-tuning bakes knowledge into model weights. Costs, freshness, citations, data protection — a practical comparison for enterprises.

Last updated: 2026-07-28

RAG (retrieval-augmented generation) keeps your knowledge in a searchable index and retrieves it at question time; fine-tuning trains the knowledge into the model's weights. For enterprise document intelligence the two are not interchangeable — and for most document-heavy use cases, RAG is the right default.

The comparison

RAGFine-tuning
New documents visibleImmediately after ingestionOnly after retraining
Citations to sourcesNative — every answer cites passagesImpossible — knowledge is diffused into weights
Access controlEnforceable per document at query timeNone — the model knows what it knows for everyone
Deleting a document's knowledgeDelete from the indexPractically impossible without retraining
Hallucination riskReduced — grounded in retrieved textPersists; model answers confidently from stale weights
Up-front costIngestion & indexingTraining runs, eval, re-deployment per update
Best forFacts, documents, policies, contractsStyle, format, domain behaviour

The compliance angle usually decides it

Under data-protection law (nFADP/GDPR), fine-tuning on personal data is a minefield: the data becomes irreversibly embedded in the weights, deletion rights become unenforceable, and per-user access control is impossible. RAG keeps data in a governed store where encryption, ACLs, retention and deletion all remain enforceable — which is why regulated Swiss industries deploy RAG, not fine-tuned corporate models.

When fine-tuning (or both) makes sense

Fine-tuning earns its cost when you need consistent behaviour rather than knowledge: a specific report format, a house tone, a narrow classification task. The strongest enterprise pattern is a capable base model + RAG for knowledge, optionally with light tuning for format — never fine-tuning as the knowledge store.

What SovraRAG does

SovraRAG is a RAG platform by conviction: hybrid retrieval with a knowledge graph, agentic multi-step reasoning for complex questions, per-document ACLs and cited answers — on 100% Swiss infrastructure, with per-tenant encryption the operator cannot bypass.

See it on your own documents

Get a guided demo and a proof-of-concept with your corpus — on Swiss infrastructure from day one.