They solve different problems
Retrieval-augmented generation supplies changing or private knowledge at request time. Fine-tuning changes model behavior, format, style, or task specialization. Treating them as substitutes usually produces the wrong architecture.
Begin with evidence
If an answer must cite a current policy, contract, manual, or record, retrieval is usually the foundation. It keeps source material outside model weights and makes evidence visible to users and evaluators.
Use tuning deliberately
Fine-tuning becomes relevant when examples can teach repeatable behavior: classification boundaries, structured output, domain language, or tool-selection patterns. It does not reliably turn a model into a current database.
The production answer can be both
Mature systems may retrieve authoritative knowledge while using a tuned model for consistent behavior. The decision should follow evaluation results, latency, cost, governance, and update frequency.
Apply this thinking to a real system.
We can map the operating constraint, evidence requirement, and safest path to a representative proof.
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