Last updated: August 2026
RAG vs Fine-Tuning: Which for Enterprise LLMs?
Use RAG when the knowledge changes frequently, answers must cite a verifiable source, or the data is proprietary and needs to stay in your own infrastructure. Use fine-tuning to teach a model a new writing style, a rigid output format, or a specialized reasoning pattern that no amount of retrieved context can provide. The two are frequently combined, not mutually exclusive — a fine-tuned model that also retrieves grounded facts via RAG is a common production pattern for enterprise deployments.
What problem does each one actually solve?
RAG solves a knowledge problem: the model needs facts it wasn't trained on, or facts that change too often to bake into training. Fine-tuning solves a behavior problem: the model needs to consistently respond in a specific style, follow a rigid structured output format, or perform a specialized reasoning task the base model doesn't naturally do well. Teams frequently reach for fine-tuning when they actually have a knowledge problem — trying to "teach" the model new facts through fine-tuning is possible but far less reliable and far more expensive to keep current than simply retrieving those facts at query time.
Which is cheaper to keep up to date?
RAG, decisively. Updating a RAG system's knowledge means re-indexing a document — instant, no retraining required. Updating a fine-tuned model's knowledge means running another training pass, which costs real compute time and requires re-validating the model's behavior didn't regress elsewhere. For any enterprise use case where the underlying information changes on any regular cadence — pricing, policy documents, product catalogs, regulatory guidance — RAG's update cost advantage compounds significantly over the life of the system.
Which is safer for regulated industries?
RAG has a structural advantage for auditability: because the model's answer is grounded in a specific retrieved chunk, the system can cite exactly which source document produced a given answer, which matters enormously in fintech and healthcare where an incorrect or unverifiable answer has real compliance consequences. A fine-tuned model's knowledge is baked into its weights — there's no way to point to the exact source of a specific fact in its output, which makes it harder to audit and harder to correct a single wrong fact without a full retraining cycle.
Full comparison
| Dimension | RAG | Fine-Tuning |
|---|---|---|
| Best for | New, changing facts | New style, behavior, format |
| Update cost | Instant re-index | Full retraining pass |
| Source attribution | Native, citable | None — opaque |
| Infrastructure | Vector database + retrieval pipeline | Training infrastructure, simpler serving |
| Auditability | High — traceable to source | Low — baked into weights |
What Code Ninety does
Code Ninety defaults to RAG for fintech and healthcare deployments specifically because of the source-attribution and auditability requirements those industries carry, reserving fine-tuning for cases where a client genuinely needs a specific output style or specialized behavior that retrieval alone can't produce. Both approaches are deployed under the same SOC 2 Type II data-handling controls. Code Ninety operates as an AI services agency under SOC 2 Type II controls. See the AI automation case study for a production deployment of these patterns.