Guide · Updated October 2026
Most enterprise AI projects start with the same question: should we fine-tune a model on our data, or retrieve our data at the moment of the question? The two solve different problems. Choosing by the problem saves months.
What each one does
Retrieval-augmented generation (RAG) leaves the model as it is. When a question arrives, the system finds the relevant passages in your documents and hands them to the model along with the question. The model answers from what it was shown.
Fine-tuning continues training the model on your examples, so its behaviour changes: its tone, its output format, or a narrow skill such as classification or extraction.
A useful shorthand: RAG changes what the model knows at the moment of the question. Fine-tuning changes how the model behaves.
Side by side
| RAG | Fine-tuning | |
|---|---|---|
| Best for | Answering from your documents and data | A consistent style, format, or narrow task |
| Keeping knowledge current | Update the index; no retraining | Retrain to add knowledge |
| Showing sources | Can cite the passages it used | Cannot point to a source |
| Data you need | Your documents, in reasonable shape | A large set of high-quality labelled examples |
| Time to a first version | Usually shorter | Longer: data preparation, training, evaluation |
| Access control | Retrieval can respect each user's permissions | What it learned is available to every user |
| Typical failure | Retrieves the wrong passage | Learns the mistakes in its examples |
Start with RAG when
- The answers live in documents that change.
- Users need to see where an answer came from, as they do in regulated work.
- Different users are allowed to see different data.
- You need a first version in weeks.
Consider fine-tuning when
- The task is narrow and repetitive: classification, extraction, or a strict output format.
- Prompting and few-shot examples have been tried properly and have stopped improving.
- You have a large set of high-quality labelled examples. Our rule of thumb is at least a thousand.
- Latency or cost per call matters enough to justify a smaller, specialised model.
They are not exclusive
Mature systems often use both: retrieval for knowledge, and a fine-tuned or carefully prompted model for form. The order matters. Get retrieval and evaluation working first, and fine-tune only when you can measure what it improves.
What decides success either way
Evaluation. Whichever route you take, build a fixed test set of real questions with known good answers, and score every change against it before it ships. Without that you cannot tell whether a new prompt, a different chunking strategy, or a fine-tuned model made things better or worse.
How we approach it at TechMaven
Most of our production work uses RAG with prompt engineering and few-shot examples, which is faster, cheaper, and easier to evaluate. We fine-tune when the data and the task justify it: a thousand or more high-quality labelled examples, and a task that prompt engineering cannot reach.
Our RAG pipelines require citations, so the model shows which passage supports each claim, and every change to an LLM feature is scored against a frozen test set before it is deployed. The Neugo case study shows this kind of system in regulated use, and our AI solutions page lists the stack. If your team is also adopting AI coding tools, read why AI-generated code still needs a senior review.