When an LLM feature underperforms, teams often jump to a single question: “Should we fine-tune?” In practice, most performance issues are caused by missing context, unclear boundaries, or weak evaluation—not a lack of training data.
Fine-tuning and retrieval-augmented generation (RAG) solve different problems. Fine-tuning changes the model’s behaviour. RAG changes what the model can see at inference time. Many successful enterprise systems use a hybrid approach, but only after they are clear on what each technique is responsible for.
Use RAG when the “truth” lives outside the model
RAG is the default for enterprise knowledge because it keeps answers tied to sources that can be governed, updated, and audited. Prefer RAG when:
- Freshness matters. Policies, prices, procedures, and product information change frequently.
- You need citations. Trust requires showing sources (see citations and grounding).
- Access control matters. Permissions are enforced in retrieval, not in prompts.
- You need operational control. You can fix content and indexing without retraining a model.
RAG is not free. It introduces a new supply chain (ingestion, chunking, metadata, ranking) and its own failure modes (see common RAG failures and ingestion pipelines).
Fine-tune when you need behaviour, not facts
Fine-tuning is helpful when the core issue is style, structure, or consistent task execution—not missing knowledge. It can be a good fit when:
- Outputs must follow strict formats. Especially for classification and extraction (paired with structured output validation).
- You need domain tone and vocabulary. E.g., consistent customer support tone across channels.
- You want fewer prompts. Reduce prompt complexity and token overhead.
- You want capability improvements. Improve performance on narrow, repeatable tasks with labelled data.
Fine-tuning increases governance burden: you must track training data provenance, versions, and regression risk. Treat it like a product release with evaluation gates and rollback paths (see canary rollouts and drift monitoring).
Hybrid patterns are common (and should be intentional)
A pragmatic hybrid approach is: use RAG for facts and citations, and use fine-tuning to improve decision boundaries and output discipline. For example, fine-tune a classifier that decides when to retrieve, when to refuse, or which tool to call, then use RAG for grounded answers.
Decide with evaluation, not opinions
Before choosing an approach, build an evaluation harness that can separate retrieval failures from generation failures. If retrieval recall is low, fine-tuning will not fix it. If generation is inconsistent despite strong evidence, better prompting, schemas, or fine-tuning may help. Use repeatable evaluations (see evaluation loops and evaluation metrics).
In most enterprises, the fastest path to better outcomes is: fix retrieval and context first, then consider fine-tuning where behaviour still needs improvement.