Glossary
RAG vs fine-tuning
RAG vs fine-tuning is the comparison between two ways of adapting a large language model to an organization's own data. Retrieval-augmented generation leaves the model unchanged and supplies relevant documents with each question at query time. Fine-tuning changes the model itself by training it further on domain examples.
The two act on different parts of the system, solve overlapping problems, and are often combined.
Why the choice matters for infrastructure
The decision is usually framed as a modelling question, but its largest consequence is where the organization's knowledge ends up. With RAG it lives in a corpus and a set of indexes that the platform team manages, updates daily, permissions and deletes from. With fine-tuning it lives in model weights produced by a GPU training run, and changing it means another run. The two approaches also produce different cost curves: RAG adds cost to every query, fine-tuning concentrates cost in each training cycle.
How each approach works
RAG parses, chunks, embeds and indexes the organization's documents. At query time it retrieves the passages most relevant to the question and places them in the prompt, and the model answers from them. Updating the system's knowledge means updating the index, one document at a time if needed. The components are described under RAG architecture.
Fine-tuning continues training a pre-trained model on a smaller, task-specific dataset: question and approved-answer pairs, examples of the required output format, or raw domain text. Full fine-tuning updates every weight in the model. Parameter-efficient methods freeze the original weights and train a small set of added parameters, which cuts GPU memory and produces a small adapter file in place of a full copy of the model.
How the two compare
| Property | RAG | Fine-tuning |
|---|---|---|
| Where knowledge is held | External corpus and indexes | Model weights |
| Updating knowledge | Re-index changed documents | Retrain and redeploy |
| Citing a source | Retrieved passages can be shown | No link from output to training example |
| Per-user access control | Enforced by filtering retrieval | Not possible inside one model |
| Removing a fact | Delete the document and its chunks | Retrain without it |
| Data required | Existing documents, unlabelled | Curated examples, often labelled |
| Cost per query | Higher: retrieval plus longer prompts | Same as the base model |
| Typical failure | The relevant passage is not retrieved | Confident answers from outdated or blended training data |
RAG's per-query cost comes from prompt length. Five retrieved passages of 500 tokens add 2,500 input tokens to every question; at one million questions a day that is 2.5 billion extra tokens daily. Fine-tuning's cost comes from training and its outputs: a 70-billion-parameter model stored at 16-bit precision is about 140 GB per checkpoint, and a training run saves several.
Combining the two
RAG is generally the stronger way to give a model facts, especially facts that change. Fine-tuning is the stronger way to change behaviour: output format, tone, adherence to a schema, domain vocabulary, or performance on a narrow task such as classification or extraction. Many production systems use both. The generator is fine-tuned to cite in a set format and to decline when the context lacks an answer, the embedding model is fine-tuned on domain questions to improve retrieval, and the facts stay in the index.
What RAG vs fine-tuning means for AI infrastructure teams
The two approaches load storage in different ways. RAG needs a living corpus that is ingested continuously, a set of derived indexes rebuilt when models change, and fast small reads at query time for chunks and citations. Fine-tuning needs curated datasets read at high throughput by GPU nodes during training, and bursts of large writes each time a checkpoint is saved. An organization running both, which is the common case, serves both patterns from the same data estate.
Control is the deciding factor for many enterprises. Knowledge that changes weekly, that is restricted to certain users, or that may be subject to an erasure request fits RAG, because a fact absorbed into model weights cannot be removed on its own or hidden from one group of users. Retraining is the only remedy, and retraining takes the original dataset, which therefore has to be kept.
Keeping one authoritative copy of the corpus avoids a quieter problem: drift between the documents a model was tuned on and the documents RAG retrieves. When both draw from the same versioned store, a team can state exactly which revision of the data each model and each index was built from.
Scality and model adaptation data
Scality's ADI page states that "High-concurrency S3 and S3 over RDMA data paths keep training, inference, retrieval, and agentic workflows fed." Scality RING stores corpora, training datasets and model checkpoints as S3 objects on standard x86 servers, up to 300 billion objects in one RING, so the documents an index is built from and the examples a model is tuned on can share one store.