RAG, Fine-Tuning, or Both?

The Dilemma with Your Own Data

A language model knows a lot about the world. But it knows nothing about a company's internal knowledge base, the latest compliance policy, or last week's product catalog. Without continuous access to current data, LLMs "invent" answers based on their training patterns [1]. Anyone looking to solve this hallucination problem and connect LLMs with their own data faces a fundamental decision: Should the model retrieve information at runtime from external sources (Retrieval-Augmented Generation, or RAG)? Or should it be specialized through targeted retraining on company-owned data (Fine-Tuning)?

Both approaches address the problem, but in fundamentally different ways [1].

RAG: The Recommended Starting Point

AWS, Oracle, IBM, and Glean reach the same conclusion: for most enterprise applications, RAG is the right starting point [1, 2, 3, 6]. The principle is elegant: with every user query, the system searches a knowledge base via semantic search, combines the retrieved information with the original query, and generates a contextually grounded answer [1]. The underlying model remains unchanged [4].

The reasons for this recommendation are solid. RAG integrates new documents in minutes rather than hours or days [2]. It requires no Data Scientists and no specialized knowledge of LoRA or PEFT [2]. And it delivers something Fine-Tuning cannot provide by design: source references that make every answer traceable [2, 3].

Academic research backs this recommendation with hard numbers. Ovadia et al. showed that RAG consistently outperforms unsupervised Fine-Tuning, both for existing and entirely new knowledge. LLMs struggle to acquire new factual information through unsupervised Fine-Tuning alone [10]. Lakatos et al. quantified the advantage across multiple models (GPT-J-6B, OPT-6.7B, LlaMA, LlaMA-2): 16% better ROUGE scores, 15% better BLEU scores, and 53% higher cosine similarity. Only on the METEOR score did Fine-Tuning perform 8% better, suggesting greater linguistic variation in outputs [11]. For rarely occurring knowledge, the gap grows even larger. Soudani et al. examined performance on less popular facts across twelve language models of varying sizes and found that RAG beats Fine-Tuning here by a clear margin. The authors also propose "Stimulus RAG" as a more efficient alternative that eliminates costly Fine-Tuning steps entirely [9].

For companies with sensitive data, there is an additional advantage. With RAG, proprietary information stays in a secured database under the organization's control, not embedded in model weights [3, 5]. Access can be updated, removed, or restricted without retraining the entire model. This is critical in regulated industries [3]. Salemi and Zamani confirmed the data privacy advantage empirically: RAG-based personalization achieved a 14.92% improvement over the baseline, while Parameter-Efficient Fine-Tuning achieved only 1.07%. Combined, both reached 15.98%, with RAG contributing the lion's share [12].

When Fine-Tuning Pays Off

Does that mean Fine-Tuning is unnecessary? Not at all. Once the focus shifts from facts to behavior, the picture reverses. Fine-Tuning continues training pre-trained models on smaller, focused datasets and embeds domain-specific terminology, compliance-conformant style, and consistent output formats directly into the model weights [1, 3, 4, 6, 7]. Concrete use cases include: clinical note interpretation in healthcare, results analysis in finance, and contract risk identification in legal [6]. In these regulated industries, where domain-specific reasoning and consistent tone are required, Fine-Tuning is the right approach [6, 7]. Fine-Tuning also excels in high-volume applications: sub-second latency instead of the 1 to 3 seconds RAG incurs through the retrieval step [5]. And unlike RAG, Fine-Tuning adds no additional overhead at runtime [3].

The price, however, is steep. Fine-Tuning is compute-intensive, requires powerful GPU infrastructure, and specialized expertise [1, 2, 3]. Parameter-Efficient Fine-Tuning (PEFT) with methods like LoRA reduces the effort significantly [1], but hits fundamental limits when it comes to knowledge injection. Pletenev et al. systematically examined how many new facts a LoRA adapter can absorb before the model degrades. Up to 500 unknown facts, the models learned with 100% reliability. Beyond that, quality collapsed. At 3,000 facts, the model reached only 48% reliability even after 10 training epochs. The MMLU benchmark dropped from 0.677 to as low as 0.554, and the models lost the ability to express uncertainty: the number of refused answers fell from over 3,000 to near zero. At the same time, answer diversity collapsed dramatically. Similar degradation patterns appeared with Mistral-7B [15].

The core principle boils down to a simple formula: RAG for facts, Fine-Tuning for behavior [1, 3, 6].

The Hybrid Approach: More Than the Sum of Its Parts?

If RAG and Fine-Tuning have complementary strengths, combining them is the logical next step. Balaguer et al. from Microsoft Research showed in an agricultural domain case study that the effects are indeed cumulative: Fine-Tuning increased accuracy by 6 percentage points, RAG contributed another 5 percentage points. For geographic knowledge transfer, answer similarity improved from 47% to 72% [8].

The most convincing hybrid approach to date comes from the RAFT framework (Retrieval Augmented Fine Tuning) at UC Berkeley. The idea: the model is trained not only on correct documents but also on irrelevant distractors, learning Chain-of-Thought reasoning with explicit citations. On the HotpotQA benchmark, RAFT achieved 35.28%, compared to 4.41% for the conventional approach of domain-specific Fine-Tuning plus RAG [13].

A counterintuitive detail: training exclusively on relevant documents was suboptimal. Only occasional exposure to irrelevant distractors improved the model's robustness [13]. Chain-of-Thought reasoning alone contributed 9.66 to 14.93 percentage points to the improvement [13].

Yet hybrid is not automatically better. Lakatos et al. found that naively combining fine-tuned models with RAG actually worsened performance [11]. The explanation lies in implementation quality: RAFT trains deliberately with distractors and structured reasoning, while an unstructured combination can confuse the models.

The value of targeted combination also shows in combating hallucinations. When RAG systems find no relevant information, downstream models tend to hallucinate [14]. Lee et al. developed Finetune-RAG, an approach that explicitly trains language models for this situation by simulating real retrieval imperfections in the training dataset. The result: 21.2% improvement in factual accuracy over the base model [14]. A glimpse into the future comes from LAG (LoRA-Augmented Generation): large libraries of specialized LoRA adapters are dynamically selected per token at runtime and combined with RAG. In experiments with 1,000 knowledge adapters, Fleshman and Van Durme achieved 95.0% of theoretical optimal performance, surpassing every individual approach [17].

The Decision in Practice

The consensus across vendors and research recommends a progressive approach in three stages [2, 3, 5, 7]:

Stage 1: Prompt Engineering. Test what the base model can already achieve with good prompts.

Stage 2: Add RAG. When the model lacks factual knowledge, set up a retrieval layer. New documents can be integrated in minutes [2].

Stage 3: Fine-Tuning when needed. Only when RAG delivers the right information but the style or reasoning is off does targeted Fine-Tuning become worthwhile [3, 7].

Oracle proposes six key questions for the decision: Does the application need current data? Do you operate in a specialized industry? Is data privacy critical? Do answers need a specific tone? Are runtime resources limited? Do you have AI infrastructure and ML talent? Depending on the answers, the choice falls on RAG, Fine-Tuning, or the combination [3].

Matillion points to an often overlooked aspect: both approaches carry hidden follow-up costs that multiply at enterprise scale [5]. With RAG, vector database storage, embedding computation, and scaling of the retrieval infrastructure add up. With Fine-Tuning, ongoing costs arise from model versioning, A/B testing infrastructure, periodic retraining cycles, and specialized talent acquisition that create technical debt [5]. The decision between RAG and Fine-Tuning is therefore not just a technical question but reflects the organization's data maturity, available expertise, and long-term budget priorities [3, 5].

A reassuring finding comes from Capital One's industry research: those who choose Fine-Tuning within a RAG pipeline need not worry much about the specific strategy. Whether independent, joint, or two-phase Fine-Tuning -- the results in Exact Match and F1 score are nearly identical. The recommendation: choose the strategy based on compute efficiency and available resources, not expected performance [16].

Conclusion

The research landscape from 2024 to 2026 paints a consistent picture across 17 sources: RAG for dynamic knowledge, Fine-Tuning for stable behavior, and the combination only with careful implementation [1, 2, 3, 6, 11, 13]. Starting with RAG minimizes cost and complexity. Adding Fine-Tuning should be a deliberate choice -- for style and tone, not as a knowledge store. The most effective AI strategies align with the company's current state and evolve with its requirements [6].

Open questions remain. Longitudinal cost comparisons in real enterprise deployments are missing. Most studies use models with 7 to 13 billion parameters; how the trade-offs shift with frontier models is barely explored [11, 15]. Multimodal scenarios involving images, audio, or tables are practically uncharted [11]. And integration into agent-based systems with multi-step reasoning is only just beginning. Mitrix sees the next convergence point here: fine-tuned models for specialized tasks, RAG for up-to-date information, and agents for orchestration [7].

But the ground rule for getting started is clear: start with RAG, expand deliberately when the need is real.

References

[1] Belcic, Ivan; Stryker, Cole (2025). "RAG vs. Fine-tuning". *IBM Think*. https://www.ibm.com/think/topics/rag-vs-fine-tuning

[2] AWS Prescriptive Guidance Team (2024). "Comparing Retrieval Augmented Generation and Fine-tuning". *AWS Prescriptive Guidance*. https://docs.aws.amazon.com/prescriptive-guidance/latest/retrieval-augmented-generation-options/rag-vs-fine-tuning.html

[3] Erickson, Jeffrey (2024). "RAG vs. Fine-Tuning: How to Choose". *Oracle*. https://www.oracle.com/artificial-intelligence/generative-ai/retrieval-augmented-generation-rag/rag-fine-tuning/

[4] Hoppa, Jocelyn (2024). "Knowledge Graphs and LLMs: Fine-Tuning vs. Retrieval-Augmented Generation". *Neo4j Developer Blog*. https://neo4j.com/blog/developer/fine-tuning-vs-rag/

[5] Funnell, Ian (2025). "RAG vs Fine-Tuning: Choosing the Right Data Strategy for AI in the Enterprise". *Matillion Blog*. https://www.matillion.com/blog/rag-vs-fine-tuning-enterprise-ai-strategy-guide

[6] Baladi, Stephanie (2026). "RAG vs. LLM fine-tuning: Which is the best approach?". *Glean Blog*. https://www.glean.com/blog/rag-vs-llm

[7] Koteshov, Dmitri (2025). "LLM Fine-tuning vs. RAG vs. Agents: A Practical Comparison". *Mitrix Technology Blog*. https://mitrix.io/blog/llm-fine%E2%80%91tuning-vs-rag-vs-agents-a-practical-comparison/

[8] Balaguer, Angels et al. (2024). "RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture". *arXiv (Microsoft Research)*. https://arxiv.org/abs/2401.08406

[9] Soudani, Heydar; Kanoulas, Evangelos; Hasibi, Faegheh (2024). "Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge". *ACM SIGIR Asia Pacific 2024*. https://arxiv.org/abs/2403.01432

[10] Ovadia, Oded et al. (2023). "Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs". *arXiv*. https://arxiv.org/abs/2312.05934

[11] Lakatos, Robert et al. (2025). "Investigating the Performance of RAG and Domain-Specific Fine-Tuning for AI-Driven Knowledge-Based Systems". *Machine Learning and Knowledge Extraction*. https://arxiv.org/abs/2403.09727

[12] Salemi, Alireza; Zamani, Hamed (2024). "Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models". *arXiv*. https://arxiv.org/abs/2409.09510

[13] Zhang, Tianjun et al. (2024). "RAFT: Adapting Language Model to Domain Specific RAG". *arXiv (UC Berkeley)*. https://arxiv.org/abs/2403.10131

[14] Lee, Zhan Peng; Lin, Andre; Tan, Calvin (2025). "Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation". *arXiv*. https://arxiv.org/abs/2505.10792

[15] Pletenev, Sergey et al. (2025). "How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?". *arXiv*. https://arxiv.org/abs/2502.14502

[16] Lawton, Neal et al. (2025). "A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation". *arXiv (Capital One)*. https://arxiv.org/abs/2510.01600

[17] Fleshman, William; Van Durme, Benjamin (2025). "LoRA-Augmented Generation (LAG) for Knowledge-Intensive Language Tasks". *arXiv*. https://arxiv.org/abs/2507.05346