Enterprise RAG
2026-09-26 · 8 min read
Key takeaways
- Use RAG when answers depend on current, permissioned company knowledge.
- Use fine-tuning to change style, format or narrow task behaviour, not to store facts.
- Most enterprise systems combine both, with retrieval as the source of truth.
ERP, PLM, CRM and documents keep provenance and version.
The short answer
Retrieval-augmented generation (RAG) fetches relevant, authorised documents at question time and passes them to the model as evidence. Fine-tuning changes the model's weights with training examples.
For enterprise knowledge that changes weekly — prices, policies, stock rules, product data — RAG is usually the right default because content can be updated, cited and restricted without retraining.
Comparison on five enterprise criteria
Freshness: RAG reflects a document as soon as it is re-indexed; a fine-tuned model reflects its last training run.
Permissions: RAG can filter evidence by user role before generation; fine-tuned weights cannot forget what one user is not allowed to see.
Citations: RAG can show the exact sources used; fine-tuning cannot attribute an answer to a document.
Cost: RAG shifts cost to indexing and retrieval; fine-tuning adds training, evaluation and re-training cycles.
Governance: RAG keeps knowledge in auditable stores; fine-tuning embeds it in an opaque artefact.
When fine-tuning still helps
Fine-tuning is useful for consistent output formats, domain vocabulary, classification tasks and reducing prompt length at high volume.
In Fashion & Retail it can teach a model house terminology or a structured proposal format, while facts about collections, stores and stock still come from retrieval.
A practical decision path
Start with RAG on one recurring decision, measure retrieval relevance and answer faithfulness, then fine-tune only if a stable, measurable behaviour gap remains.
Keep human approval for actions above threshold regardless of the technique.
FAQ
Frequently asked questions
Is RAG better than fine-tuning for enterprise knowledge?
For knowledge that changes and must respect permissions and citations, RAG is usually the better default. Fine-tuning suits stable behaviour such as format or vocabulary.
Can RAG and fine-tuning be combined?
Yes. A common pattern uses a fine-tuned model for format and tone while retrieval supplies current, authorised facts with citations.
Does RAG remove hallucinations?
It reduces them by grounding answers in evidence, but it does not eliminate them. Faithfulness and citation checks are still required.
How much data is needed to start enterprise RAG?
You can start with the document set behind one decision, provided sources are current, owned and access-controlled.