Retrieval-augmented generation vs. fine-tuning: How to choose the best method to optimize AI performance

Trained on massive datasets to identify patterns, large language models (LLMs) like Midjourney or GPT-4 are highly effective at generalizing knowledge and producing supportive outputs for a wide range of tasks. However, when prompted on more specialized problems these models tend to underperform off the shelf.

This is because, while foundational LLM training is extensive, models aren’t trained on niche skills your organization may need—like providing technical product support, interpreting diagnostic imagery, or forecasting business performance based on private financial records. For use cases like these, third-party models are likely to hallucinate, generating irrelevant or incorrect responses in an attempt to fill knowledge gaps.

Enterprises can mitigate these limitations by leveraging retrieval-augmented generation (RAG) or model fine-tuning. These are the two primary techniques AI practitioners use to keep outputs accurate, up-to-date, and capable of handling domain-specific queries. 

While both approaches support more reliable outputs, each has distinct requirements and AI performance outcomes. Organizations must weigh their use case requirements and resources against each option to select the optimal solution. 

How retrieval-augmented generation and fine-tuning work 

RAG and fine-tuning improve model performance through different methods. While RAG lets a model retrieve new, external information as needed, fine-tuning continues the model’s training. 

Bridge knowledge gaps with RAG 

RAG enables an LLM to access and use new context and information that wasn’t available in its original training data. With RAG, developers create a knowledge base, usually from a collection of documents or datasets containing relevant material for the desired task. Then, they build a retrieval architecture (for example, a knowledge graph or vector database). This allows the model to retrieve insights before responding to user prompts. 

Fine-tune AI models through specialized training 

Unlike RAG, which keeps the original model architecture intact, fine-tuning changes the model itself by training it on additional smaller datasets curated for unique tasks. As part of this process, developers adjust the model’s weights, parameters, and other architectural components, tailoring the model to perform optimally on new data.

The goal of fine-tuning is to harness the functionality of the original model while updating its knowledge for more targeted problems. Fine-tuning is more efficient and cost-effective than training a model from scratch. It’s also ideal if the new, more specialized domain offers insufficient data to build a new model. 

RAG vs. fine-tuning: 6 key distinctions 

Both RAG and fine-tuning help improve model performance for more specialized use cases. However, each approach will have a different impact on your AI initiative in terms of cost, scalability, and other performance measures. 

1. Output quality and reliability 

According to comparative research, both approaches perform well on niche tasks, but RAG stands out in its ability to generate context-rich outputs. RAG is also particularly strong at minimizing hallucination, helping ground responses in factual information. This helps offset an LLM’s tendency to improvise or make “best guesses” when there’s a gap in its training data, a limitation still present in fine-tuned models.

While providing more context means that RAG addresses hallucination, the technique can also generate more verbose outputs than fine-tuning. This may not be ideal for use cases where users prefer more concise results. 

2. Model transparency 

AI practitioners can easily view LLM inputs and outputs, but intermediary algorithms and reasoning processes are often opaque. This makes it challenging for enterprises to report on how their models use data or generate certain outputs. Model transparency is an important aspect of responsible AI, enabling compliance with data privacy laws and regulations. It’s also necessary for use cases where customers may want to understand how or why models played a role in decision-making. 

Because RAG uses a structured, easily navigable knowledge base, the approach makes tracing an output’s origin relatively easy. Fine-tuning extends the “black box” nature of LLMs, which can perpetuate transparency concerns for enterprises. 

3. Scalability 

Fine-tuning LLMs is highly scalable for different training data volumes, model sizes, or available enterprise resources. Developers can expand or reduce the scope of new datasets, parameters, and other model components as needed, with the caveat that larger datasets will have higher computational demands. With techniques like quantization and model pruning, developers can also streamline large models to reduce latency and run on smaller hardware, like mobile devices. 

RAG may not perform as well for larger datasets due to its retrieval process. AI models enhanced with RAG must perform the extra step of retrieving external information before generating outputs. This can increase latency, especially when the knowledge base is large. 

4. Knowledge and behavior adaptability 

The external sources from which RAG models retrieve new information can be easily updated as new data becomes available. This makes the technique ideal for dynamic use cases where knowledge changes quickly. For example, the subjects of technology, medicine, current events, or the economy change frequently. RAG shines in scenarios with constantly evolving standards of knowledge, domain expertise, or ethics.

On the other hand, fine-tuned models are constrained by what they learn over a finite training period. Even after a model is tuned, this new data will eventually become outdated, warranting further training. Although RAG is more adaptable for knowledge acquisition, fine-tuning is better for adapting a model’s tone, writing style, and other core behavioral features.  

5. Speed to market 

With fine-tuning, speed to market depends on an enterprise’s available compute resources. For example, organizations must use graphics processing units (GPUs) to fine-tune models. The latest, most powerful GPU versions support much faster training than older GPUs or central processing units (CPUs) but are also more expensive. Enterprises can access efficient GPU instances through cloud providers, but high demand and limited availability means you may need to plan months ahead for model training. Compared to fine-tuning, RAG has fewer computational requirements, making GPU availability less of a concern for speed to market.  

Development techniques and dataset size can also have an impact on speed to market. Large datasets require more time to cleanse and label in preparation for RAG or fine-tuning. Developers may access AI tools to expedite development. For example, LLMs can automatically retrieve relevant data for your application and suggest optimal model configurations or RAG architectures. This minimizes manual work, significantly reducing development timelines for either methodology.  

6. Costs 

One of the biggest differentiators between RAG and fine-tuning is cost. RAG is typically much less expensive with most of its expenses emerging from data management and retrieval architecture development. Enterprises using RAG may also invest in LLM partnerships to use a foundational model.

Even though fine-tuning requires less data than training a model from the ground up, the approach still has significant computational demands, which increases costs. To start, fine-tuning usually requires larger labeled datasets than RAG. There’s also the issue of GPU cost and availability, which can be a major barrier for many enterprises. While organizations can build their own compute infrastructure to avoid GPU bottlenecks with cloud providers, maintaining this infrastructure in-house is a significant long-term investment.

Finding the best-fit AI training technique for your enterprise 

Evaluate each solution against factors like your dataset, available resources, and use cases to build a strategy aligned with your AI development goals. 

Resources 

Given the development costs, RAG may be more suitable for organizations with limited financial resources. However, if fine-tuning is more appropriate for your use case, techniques like parameter efficient fine-tuning (PEFT) can help save computational costs. Rather than adjusting all of a model’s parameters, PEFT only adjusts parameters most critical to model performance.

Consider your existing development expertise before choosing a solution, since hiring and upskilling talent is also a large investment. RAG and fine-tuning development both involve distinct skill sets, with the latter requiring expertise in areas like natural language processing (NLP) and neural network design. 

Data type and volume 

Smaller datasets are ideal for RAG, while fine-tuning performs best with larger datasets. RAG applications can still handle large volumes of data—they just won’t perform as efficiently. RAG could align well if your AI system needs to reference a library of external documents for a niche task, like product documentation or medical and legal records. 

Use cases for RAG and fine-tuning 

RAG and fine-tuning both excel in different use cases. For instance, RAG is suited to quickly evolving domains like research, current events, or use cases where accuracy is paramount. If AI transparency and interpretability are important for validating your decisions and adhering to data privacy regulations, RAG is ideal.

Fine-tuning performs well for tasks relying heavily on NLP, like sentiment analysis or customer service personalization. These use cases often require adjustments to a model’s behavior, such as the tone of outputs, which fine-tuning is better suited for than RAG. Additionally, tasks that don’t need frequent information updates may perform optimally with fine-tuning. For example, training a model for specialized sentiment analysis may not rely as heavily on current domain expertise as a research- or news-focused application.

The strengths and weaknesses of RAG and fine-tuning are somewhat complementary. For some use cases, combining both techniques may be best. However, consider the increased computational demand, development complexity, and security needs for such hybrid models. 

Optimize AI performance now, stay flexible long-term 

Both RAG and fine-tuning are effective ways to improve model performance on domain-specific tasks. Performing a cost-benefit analysis weighing factors like data availability, resources, and use case requirements will help you choose a solution to optimize outputs while remaining sustainable in the long term.

It’s also important to navigate strategies like RAG and fine-tuning as an ongoing process rather than a single implementation, especially as development techniques continue advancing. Innovative organizations must regularly assess their AI system performance, as well as industry standards surrounding responsible AI, and remain vigilant and ready to adapt to new best practices.

Take a deep dive into RAG and learn how it boosts the functionality of LLMs.