Decision guide
RAG vs Fine-Tuning: Enterprise Decision Guide
Compare retrieval-augmented generation and fine-tuning for enterprise AI use cases.
Key principles
RAG connects model responses to selected external knowledge at run time; fine-tuning changes model behaviour using additional training examples.
RAG is often useful when knowledge changes frequently or permissions and source traceability matter.
Fine-tuning can be useful when consistent task behaviour, formatting or specialised patterns matter and can be demonstrated with suitable examples.
Decision checklist
✓ Start from the problem, not the technique.
✓ Evaluate retrieval quality separately from generation quality in RAG systems.
✓ Do not assume fine-tuning solves missing or unreliable source knowledge.
How DataLgorithmics approaches the decision
Define the operating problem, identify the evidence required, compare realistic options, establish success and control criteria, and make the smallest justified investment that can answer the decision.