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RAG vs fine-tuning: which should you use?
Short answer
Use RAG when the model lacks knowledge, such as your documents or recent facts, and fine-tuning when it lacks a behaviour, such as a style or a fixed output format. RAG keeps knowledge in documents you can update and cite; fine-tuning bakes patterns into the model itself.
With troveGEN
troveGEN is the retrieval choice: your knowledge stays in documents you can update, cite and delete, and it works with whichever language model you use.
The core difference
Fine-tuning continues a model's training on your examples, adjusting its internal weights. It is good at teaching tone, format and domain phrasing. RAG leaves the model unchanged and supplies relevant passages at question time. It is good at facts, especially facts that change or are private.
Compare them on what matters
- Freshness: RAG reflects a document the moment it is updated. A fine-tuned model is frozen at training time.
- Traceability: RAG can cite the passage behind an answer. A fine-tuned model cannot say where a fact came from.
- Cost to update: change a document and re-index it, versus another training run.
- Privacy and deletion: with RAG, deleting a document removes the knowledge. Removing something from trained weights is hard.
- Behaviour: fine-tuning is the better tool for consistent tone, structure and specialised phrasing.
Why fine-tuning is a poor knowledge store
Facts learned from training examples are not reliably memorised. The model may blend or misremember them when generating, and there is no easy way to check. For a question like "what is our refund window" you want the exact current text, not the model's impression of it.
Using both
The approaches are not rivals. A common pattern is RAG for the facts and a lightly fine-tuned model for the house style or output format. Most teams only need RAG; a few need both; very few need fine-tuning alone.
Key takeaways
- RAG for knowledge, fine-tuning for behaviour.
- RAG stays current, is citable and supports deletion.
- Start with RAG; add fine-tuning only if style or format still falls short.
How troveGEN helps with choosing between RAG and fine-tuning
troveGEN is the retrieval side: it keeps your knowledge in documents you control, serves the right passages with their sources, and lets you delete a document and everything derived from it. It works with any model you choose to generate the final answer, fine-tuned or not.
What troveGEN provides
- Answers that cite the passage they came from
- Updates by re-ingesting a document; deletion that removes the passages and vectors
- Point-in-time retrieval to ask what a document said earlier
- Any model for the final answer: yours, ours or a self-hosted one
- Evaluation tools to compare setups on your own questions
Frequently asked questions
Is RAG cheaper than fine-tuning?
Usually, because updating knowledge means re-indexing a document rather than running a training job. RAG adds a retrieval step to each question, which is typically small compared with generation.
Can fine-tuning remove the need for retrieval?
Only for knowledge that is small, stable and public. For large, changing or private collections, retrieval is more accurate and more controllable.
Which is better for compliance?
RAG, in most cases, because every answer can point to a source and removal requests can be honoured by deleting the document.
How does troveGEN help with choosing between RAG and fine-tuning?
troveGEN is the retrieval choice: your knowledge stays in documents you can update, cite and delete, and it works with whichever language model you use. It provides: Answers that cite the passage they came from; Updates by re-ingesting a document; deletion that removes the passages and vectors; Point-in-time retrieval to ask what a document said earlier; Any model for the final answer: yours, ours or a self-hosted one; Evaluation tools to compare setups on your own questions.