If the model must answer from policies, contracts, or a changing catalog, begin by evaluating RAG. If it needs a consistent format, classification behavior, or tone, fine-tuning may fit. These are different tools rather than competing ways to solve one problem.
How RAG works
The system indexes documents, retrieves relevant passages for each question, and adds them to the model context. Its advantages include fresh information, source citations, and quick document removal. Its challenges include search quality, document chunking, and access control.
What fine-tuning changes
Fine-tuning trains a model on examples so it repeats a behavior, such as a response structure, classification method, tone, or decision pattern. It is not a dependable database for facts that change weekly, and updating or explaining a factual answer is harder.
Cost and maintenance
RAG needs a document pipeline, index, retrieval layer, and ongoing evaluation. Fine-tuning requires a quality dataset, training runs, and regression tests. Most teams should begin with prompts and retrieval, then consider fine-tuning once examples reveal a stable behavioral gap.
Protecting company knowledge
Authorization must happen before retrieval, not after an answer is generated. Every user should receive only passages they could access in the original system. Retention, encryption, deletion, and model-provider policies also need explicit decisions.
How to measure quality
Create a realistic question set with expected answers and sources. Measure retrieval, faithfulness to the source, refusal when information is absent, and response time. A convincing three-question demo is not an evaluation.