Blog · Concepts · September 20, 2026 · 7 min read
RAG vs Fine-Tuning: Making a Model Know Your Documents
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A downloaded model knows a lot, but it knows nothing about your company wiki, your notes, or last quarter’s invoices. There are two fundamentally different ways to fix that, and picking the wrong one wastes weeks. Here is the plain-English version.
RAG: look it up at answer time
Retrieval-Augmented Generation keeps your documents in a searchable index. When you ask a question, the system finds the few relevant passages and hands them to the model as context: “here are the pages, answer from these.” The model never changes — only what you hand it does.
- Instant updates: add a document, it is queryable immediately. Delete it, and it is forgotten.
- Cheapest path: no training at all. Open WebUI’s document chat is RAG with checkboxes — upload files and ask questions, entirely offline.
- Citable: good RAG setups show which passages produced the answer, so you can verify.
- Cost in VRAM: retrieval needs an embedding step and the relevant text must fit in context — see what long contexts cost in our context guide.
Fine-tuning: change the model itself
Fine-tuning continues training on your examples so the weights shift — teaching style, format, domain vocabulary, or a narrow skill. It is powerful but heavyweight: you curate hundreds to thousands of examples, run training (hours to days, ideally on a big-VRAM GPU), and manage model versions.
- Right for: consistent output formats, specialized tone, industry jargon, or making a small model behave like a specialist.
- Wrong for: facts that change. A fine-tuned model does not reliably recall specific documents and cannot be updated by editing a file — you retrain.
- Memory reality: training needs more VRAM than inference — several times the model size in working memory even with parameter-efficient methods (LoRA/QLoRA). Check what fits on your hardware in the compatibility panel before planning a run.
The decision table
| You want… | Use |
|---|---|
| Answers grounded in your changing documents | RAG |
| A specific writing style or output format | Fine-tuning (or just a good system prompt first) |
| Domain jargon handled naturally | Both: fine-tune the vocabulary, RAG the facts |
| Results today, zero training | RAG |
The common mistake is jumping to fine-tuning when a system prompt plus retrieval would do. Try the cheap ladder first: prompt → RAG → fine-tune — each rung costs more time and hardware than the last.