ModelRefs / How to choose between fine-tuning and RAG
How to choose between fine-tuning and RAG
A decision framework for choosing retrieval, fine-tuning, or a hybrid approach based on knowledge freshness, behavior adaptation, data, evaluation, cost, maintenance, and risk.
What this reference supports
How to choose between fine-tuning and RAG: This guide supports an implementation decision by organizing criteria, trade-offs, risks, evidence, and next steps. Use it alongside the related model, provider, benchmark, and workflow references.
How to choose between fine-tuning and RAG: Treat the framework as a starting point. Weight criteria for your workload, document assumptions, compare a small candidate set, and run representative evaluations before making a production commitment.
How to choose between fine-tuning and RAG: Sources and examples provide context, not guarantees. Recheck current provider documentation, data-handling terms, pricing, regional availability, and benchmark protocols when those details affect the decision.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to How to choose between fine-tuning and RAG.