"Should we fine-tune?" is one of the first questions enterprise teams ask. The second is "should we use RAG?". The right answer is not either-or. It depends on what you are trying to improve: knowledge freshness, groundedness, tone, domain language, tool behaviour, or business rules.
Use RAG when the truth changes often
RAG is strongest when the information changes: policies, procedures, product docs, and knowledge bases. RAG lets you update answers by updating content, not by retraining models. It also supports citations and verifiability (see enterprise search with LLMs and structured citations).
If freshness matters, design and measure it explicitly (see freshness evaluation and source lifecycle).
Use fine-tuning when you need behavioural consistency
Fine-tuning is strongest when you want a consistent behaviour pattern:
- Brand tone and response structure.
- Domain-specific language and jargon.
- Task patterns like summarisation, extraction, or classification.
If the requirement is "always respond in this shape", you may not need fine-tuning; you may need structured outputs and validation (see structured validation and style guides).
Use both when you need knowledge plus behaviour
Many products need both:
- RAG to provide current, customer-specific information.
- Fine-tuning (or strong prompting) to enforce tone, format, and reliable tool calling.
In this hybrid approach, RAG provides evidence and fine-tuning provides consistent use of that evidence.
Consider data sensitivity and governance early
Fine-tuning can require shipping data to providers and maintaining training datasets. This has governance implications:
- Classify training data and restrict what is allowed (see data classification).
- Define residency, retention and deletion expectations (see data residency).
- Separate evaluation datasets from training datasets and handle sensitive review carefully (see evaluation data handling).
RAG also has governance risks: connector security, permissions enforcement and content provenance (see connector hardening and tenant isolation).
Compare operational costs, not just vendor pricing
The cost question is usually framed as "tokens are expensive". The real cost includes operations:
- Fine-tuning: dataset maintenance, drift, re-training, rollout and rollback.
- RAG: ingestion pipelines, index rebuilds, freshness management and retrieval evaluation.
Either way, treat cost as an operational metric with guardrails (see cost guardrails).
Evaluate the choice with a benchmark harness
Do not decide based on intuition. Run a small benchmark:
- Define success criteria and failure modes.
- Compare RAG-only, prompt-only, and hybrid options.
- Measure groundedness and refusal behaviour on your data.
See RAG benchmark harness and testing pyramid.
Fine-tuning and RAG are not competitors. They are tools with different strengths. The best architecture is the one that matches your change rate, risk profile, and operational maturity.