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Combining fine-tuning with retrieval-augmented generation (RAG) can help an enterprise model interpret retrieved documents, follow specialized task instructions, and disregard irrelevant passages. It does not guarantee more accurate answers. The right approach depends on where the system fails: finding current evidence, using that evidence correctly, or following the required task and format.
What is the difference between fine-tuning and RAG?
RAG makes information from an external document collection available to a model at answer time. A typical system processes and indexes source documents, retrieves relevant passages for a question, and places those passages in the model’s context. When the underlying corpus changes, it can be updated without retraining the model.
Fine-tuning updates a model using training examples. Depending on the method and examples, it can shape task behavior, response format, style, or domain-specific interpretation. It does not, by itself, provide a reliable, current citation to the document supporting a particular answer. AWS’s technical guidance also cautions that fine-tuned models can hallucinate when answering questions.
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How can the two approaches work together?
In a combined system, retrieval still supplies passages at inference time, while fine-tuning can teach the model how to answer using that context. This is useful to test when the retriever finds relevant evidence but the model misreads it, overlooks key details, or is distracted by irrelevant passages.
RAFT: training for the open-book setting
The RAFT paper describes training with a question, retrieved documents, and an answer based on a relevant document. Its examples also include distractor passages, so the model can learn to identify useful evidence instead of treating every retrieved passage as equally relevant. At inference time, the model still receives retrieved documents.
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The authors report that RAFT outperformed comparison baselines on their tested PubMed, HotpotQA, Hugging Face, Torch Hub, and TensorFlow Hub datasets. Their reported RAFT (LLaMA2-7B) results were:
| Dataset | Reported score |
|---|---|
| PubMed | 73.30 |
| HotpotQA | 35.28 |
| Hugging Face | 74.00 |
| Torch Hub | 84.95 |
| TensorFlow Hub | 86.86 |
These are task-specific scores reported by Zhang et al. in the 2024 version 2 arXiv preprint; they are not a shared accuracy scale that makes scores directly comparable across datasets. They show that training to use retrieved context can help on evaluated tasks, not that it will improve every enterprise system.
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Does fine-tuning improve RAG accuracy?
It can, but the cited findings do not establish a universal gain. Results differ with the domain, model, task, retrieval corpus, baseline, and evaluation method.
| Study | Reported result | What it supports |
|---|---|---|
| Zhang et al., RAFT, 2024 | Higher reported performance than comparison baselines on the paper’s tested datasets; see the dataset-specific scores above. | Training a model to use retrieved context can help on some domain-specific tasks. |
| Avi-ad Avraam Buskila, 2026 | In a controlled medical multiple-choice comparison with a 1,273-question evaluation split, domain fine-tuning reached 53.3% majority-vote accuracy versus 46.4% for the general 4B baseline, a 6.8 percentage-point difference. The authors did not find a statistically significant gain from the tested RAG corpus. | Fine-tuning helped in that comparison; its result does not establish that fine-tuning is preferable for other tasks or document collections. |
| Sturm et al., 2026 | In a comparison using two closed automotive-industry datasets, the authors reported RAG as the most effective and cost-efficient adaptation method in their tested setting. | RAG performed best in that study’s setting, not necessarily in other enterprises or tasks. |
The apparent difference is not a contradiction: these studies tested different systems and questions. Treat fine-tuning, RAG, and a combination as candidates to compare on your own representative data, not as a ranking that applies everywhere.
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Should you fine-tune an LLM or use RAG?
Start with the failure mode and the information the system must provide. AWS recommends starting with RAG for question-answering that references custom documents, and describes fine-tuning as useful for additional tasks such as summarization. It also notes that the two approaches can be combined.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute- Choose RAG first to test when answers must draw on custom, changing documents or users need to trace answers to source material.
- Consider fine-tuning when the model needs more consistent task behavior, output conventions, or domain-specific interpretation.
- Test a combined approach when evaluation shows that useful passages reach the model but it does not reliably select or interpret the evidence, or when distractors lead it astray.
- Fix retrieval or data issues first when relevant passages are missing, outdated, incomplete, or inaccessible. Fine-tuning cannot supply evidence that the system did not retrieve.
These are decision criteria, not performance guarantees. A fine-tuned model may still need retrieval for current, document-grounded answers; RAG alone may be sufficient when the model already uses the evidence well.
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How should an enterprise evaluate RAG accuracy?
Use a held-out set of representative organizational questions with trusted answers or evidence. Compare the existing system with RAG-only, fine-tuning-only where appropriate, and combined variants. Measure retrieval separately from answer quality: otherwise, it is difficult to tell whether a wrong response came from missing evidence or from the model’s use of evidence.
- Retrieval quality: Does the system find the relevant passages, and does it miss important evidence?
- Groundedness: Does the answer stay within the supplied context instead of inventing unsupported details?
- Relevance: Does it answer the question actually asked?
- Completeness: Does it include all material information expected in a correct response? Relevance and completeness are separate evaluation dimensions in Microsoft’s guidance.
- Freshness and traceability: Can the corpus be updated, and can a reader connect an answer to its sources?
- Cost and user effort: Include generation and operational costs, as well as the extra interactions users need to get an acceptable answer.
Microsoft documents RAG evaluators for retrieval, groundedness, relevance, and response completeness; some evaluator capabilities are marked preview in its documentation. AWS documents evaluation jobs for retrieve-only and retrieve-and-generate workflows. Track regressions as well as averages, and examine failures by question type rather than relying on one headline score.
What can undermine a production system?
Accuracy depends on the whole pipeline, not just the model. Source documents may be poor quality or stale; indexing and retrieval may omit the right evidence; and even relevant passages may be used incorrectly. Fine-tuning also adds training-data preparation, model availability, and change-management work.
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Production controls should address source-data quality, document traceability, access permissions, freshness policies, versioning, automated reindexing, and governance. In particular, retrieved private material must only be available to users authorized to see it. Test the full system—documents, chunking and indexing, retrieval and ranking, prompts, model, fine-tuning data, permissions, and evaluation set—because a change to any of these can affect the answer.
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