A practical guide from DigiMartrix. Use it to frame the decision, make trade-offs visible and choose a next step that fits your context.
These approaches solve different problems
Retrieval-augmented generation (RAG) finds relevant reference material at request time and supplies it to a model. Fine-tuning changes model behavior through additional training examples. Retrieval is usually the first option to investigate when the challenge is access to changing or private reference information.
Choose retrieval for changing facts
Policies, product details, help articles and internal procedures often change. A retrieval design can use an updated source without retraining the model, but the source still needs permissions, clear document boundaries, freshness controls and tests for whether the right passages are retrieved.
Consider fine-tuning for repeated behavior
Fine-tuning may help with a consistent output format, specialized tone or a recurring transformation when examples are representative and permitted for training. It is not a reliable way to keep fast-changing facts current, and it does not remove the need to evaluate the resulting behavior.
Build an evaluation set before choosing
Collect representative requests and define what a correct answer looks like. Include hard cases, missing information and permissions. Compare a simple prompted baseline, retrieval and any proposed tuned model on answer quality, refusal behavior, latency, operational effort and total cost.
Use both only when each has a job
Some systems retrieve current evidence and also use a tuned model for a stable task pattern. Add each component only when evaluation shows a specific benefit that justifies its maintenance and risk.
A practical sequence
Clean and permission the source data. Improve retrieval and prompts. Measure against a held-out evaluation set. Investigate fine-tuning only if a remaining behavior problem is clear and training examples can address it safely.