Use retrieval when answers depend on changing or private evidence. Fine-tune when you need repeatable behavior, style or task performance. Many production systems combine both, but each must earn its complexity.
They solve different problems
RAG supplies evidence to a model at request time. Fine-tuning changes the model's behaviour by training it on examples. If the problem is current private knowledge, retrieval is usually the first tool. If the problem is consistent format, tone or task behaviour, fine-tuning may be appropriate.
Neither method is a factuality switch. Retrieval can surface irrelevant evidence, and a tuned model can confidently repeat patterns that no longer apply.
Choose retrieval for changing knowledge
Policies, product catalogues, customer records and technical documentation change. RAG can index new versions without retraining and can return citations. It also allows access control at query time, which is essential when users have different entitlements.
Retrieval adds its own operational work: ingestion, parsing, ranking, freshness checks and evaluation. Use it when evidence and update speed justify that system.
Choose fine-tuning for repeatable behaviour
Fine-tuning is useful when a base model understands the task but performs it inconsistently: classifying specialised text, following a stable response structure or adopting domain-specific conventions. High-quality examples matter more than raw quantity, and a held-out evaluation set is mandatory.
Do not fine-tune secrets or frequently changing facts into model weights. Removing or updating that knowledge is harder than changing a source index.
Combine them only with evidence
A tuned model can use retrieved evidence, but the combination doubles the number of things that can change. Establish a baseline prompt, evaluate retrieval, then test whether fine-tuning delivers a measurable improvement in behaviour or cost.
Compare approaches on task success, groundedness, latency, maintenance effort and total operating cost. The simplest system that meets the quality bar is normally the best production design.
Frequently asked questions
Can fine-tuning teach a model company knowledge?
It can encode patterns from company data, but it is a poor default for facts that change or require citations. Retrieval is generally easier to update, secure and audit.
Can a system use RAG and fine-tuning together?
Yes. Retrieval can provide current evidence while fine-tuning shapes task behaviour. Combine them only after each component has shown independent value.
Further reading
Explore official documentation for the tools and architecture patterns discussed in this guide.