[00] Comparison
A general language model does not know your products, policies or customers. There are two main ways to change that. Retrieval-augmented generation (RAG) looks up relevant passages from your documents at question time and gives them to the model. Fine-tuning trains the model further on examples so it learns a style, format or task.
The model looks up your documents for every answer
The model is trained further on your examples
verdict belowfaq · 04
Side by side
They solve different problems and are often combined. This guide explains what each does well, where each falls short and how we decide which to use.
| criteria | opt.aRetrieval-augmented generation (RAG) | opt.bFine-tuning |
|---|---|---|
| What it changes | What the model knows at the moment it answers, by adding relevant passages to the prompt. | How the model behaves: its tone, format, vocabulary and skill at a specific task. |
| Keeping information current | Update a document and the next answer reflects it. No retraining needed. | New facts require new training data and another training run. |
| Citations and traceability | Answers can show which document and passage they came from, so users can check them. | Knowledge is absorbed into the weights. The model cannot point to a source. |
| Data you need | Your existing documents, cleaned and split into passages. No labelled examples required. | Hundreds to thousands of good input and output examples, prepared and checked. |
| Upfront cost | Lower. Data pipeline, vector database, retrieval tuning and evaluation. | Higher. Data preparation, training runs, evaluation and model hosting or provider fees. |
| Running cost | Longer prompts mean more tokens per answer, plus a vector database to host. | Shorter prompts and sometimes a smaller model, which can lower cost at high volume. |
| Hallucination risk | Reduced when retrieval finds the right passage and the model is told to answer only from it. Weak retrieval still leads to wrong answers. | Not reduced for facts. A fine-tuned model can state wrong information confidently. |
| Access control | Can filter documents by user, so people only get answers from content they are allowed to see. | Anything in the training data is available to every user of the model. |
| Consistent format and style | Controlled by prompts and examples, which works well but can drift on complex formats. | Strong. Well suited to fixed output formats, brand voice or classification tasks. |
What RAG looks like
An illustrative run of retrieval-augmented generation on company documents. Pick a question to see which chunks are retrieved and how each sentence of the answer points back to its source.
Each question is scored against chunks of your documents and systems, and only the most relevant few are used.
The selected chunks share a limited context window with the instructions and the question, so retrieval quality matters.
The answer cites the chunk behind each sentence, so a person can check it. Update a document and the next answer changes.
Illustration: someone asks “What is our refund window for bulk orders?”. The assistant scores 6 chunks from the company's own sources, packs the 4 most relevant into its context window, and answers: “Bulk orders (50+ units per line) can be returned within 14 days of delivery. Each return needs a QC check and an RMA number before pickup, as in a recent customer request.” Each sentence cites the chunk it came from.
When to choose which
Verdict
Start with RAG and good prompts. For most business assistants, knowledge search and document tools, it gives better factual accuracy, citations and easier upkeep, at lower cost. Consider fine-tuning once you have an evaluation set showing where RAG and prompting fall short, usually on format, style, classification or specialist language. Many mature systems use both: a fine-tuned model that follows your format, fed by retrieval that keeps its facts current.
FAQ
Straight answers to what buyers ask us most. Can't find what you need?
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Ask us directlyPartly, but not reliably. Fine-tuning is better at teaching behaviour than facts, and the model cannot cite where knowledge came from. For factual answers from your documents, RAG is usually the better tool.
We build an evaluation set of real questions with expected answers, agreed with your team, and score each approach against it for accuracy, faithfulness to sources and format before anything goes live.
Yes. Open models can be fine-tuned and hosted in your own cloud account, so your training data and model stay under your control. Commercial providers also offer fine-tuning under their data terms.
Usually because retrieval found the wrong passage or none at all. Better document preparation, chunking, search tuning and an instruction to say "I don't know" fix most of these cases.
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Free consultation
Share your use case and a sample of your data, and we will recommend RAG, fine-tuning or both, with a plan to measure the result.
