“Should we use RAG or fine-tune a model?” sounds like a model-selection question. It is more useful to ask what needs to change: the information the model can use, the way it responds, or both.
Retrieval-augmented generation (RAG) provides relevant material at request time. Fine-tuning changes model behavior by training on examples. They solve different problems and can sometimes complement each other.
Reach for retrieval when knowledge changes
If answers depend on internal policies, product documentation, customer-specific records, or other information that changes over time, retrieval is often a natural fit. The application can find relevant sources and include them as context when generating an answer.
That brings its own engineering work: reliable ingestion, access-aware retrieval, useful ranking, source references, freshness, and evaluation. A RAG application is only as good as the evidence it retrieves and the way it uses it.
Consider fine-tuning when behavior is the issue
Fine-tuning may help when a model needs to follow a stable format, adopt a consistent task-specific style, or perform a repeated transformation that is difficult to steer reliably with instructions alone. The training examples need to reflect the intended behavior and be separated from an evaluation set that can reveal regressions.
Fine-tuning is not a convenient way to keep frequently changing facts current. Updating knowledge that way can make refreshes slower and harder to audit than retrieving from maintained sources.
Test before you choose
First establish representative examples of the task and a baseline. If the model gives incorrect factual answers, inspect whether the needed evidence was available and retrieved correctly. If it has the evidence but ignores the required structure or tone, then instruction design or a fine-tuning experiment may be relevant.
Compare approaches on answer quality, source correctness, operational complexity, latency, privacy needs, and cost—not just a few impressive examples. Keep tests for common cases and difficult edge cases, and repeat them whenever models, prompts, data, or pipelines change.
The short version
Use retrieval to provide the model with relevant, current information. Explore fine-tuning when you need to change stable patterns in how the model performs a task. Improve the underlying workflow when the issue is unclear requirements, inconsistent source data, or a process that should not be automated at all.
A small, measured experiment can tell you more than a large architecture decision made in the abstract. Choose the approach that addresses the actual failure mode, then keep the system simple enough to operate and improve.