RAG vs. Fine-Tuning: How to Choose

General-purpose large language models, or LLMs, have become popular with the public because they can discuss a wide variety of topics and write term papers, thank you notes, and many other tasks. In business, however, these generic outputs won’t do. An LLM that’s expected to provide tech support for a particular gadget, for example, needs to draw on domain-specific knowledge.

There are currently two ways to help generative AI models deliver responses that reflect that sort of expertise: fine-tuning and retrieval-augmented generation, or RAG. Each comes with benefits and challenges. Let’s take a deeper look at these options to understand how they work and when to use them.

Key Takeaways

• Both RAG and fine-tuning make generic AI models more useful in a particular field or for a specific use case.
• RAG gives an LLM access to a company’s internal data stores; that helps the LLM provide more targeted responses, which is critical for use cases that rely on up-to-date information.
• Common uses of RAG include tech support, inventory lookup, and retail recommendations.
• Fine-tuning uses a collection of domain-specific data to train a general-purpose LLM for a particular task. Think of medicine or coding, which have their own logic and parlance.
• Fine-tuning and RAG may be combined to give an LLM both strong domain knowledge and up-to-date information.

What Is Retrieval-Augmented Generation (RAG)?

RAG, short for retrieval-augmented generation, is an architectural framework developed by researchers at Meta to help general-purpose AI models deliver outputs that are relevant and useful to organizations. RAG does this by giving a large language model, or LLM, access to an internal knowledge base that it can use to augment its original training data. The result is an AI system that combines the language fluency of an LLM with local data to deliver targeted, contextually appropriate responses. This approach, unlike AI model fine-tuning, works without modifying the underlying model itself.

When to Use RAG

Use RAG when it’s important for generative AI responses to provide up-to-date or organization-specific data that wasn’t part of the LLM’s training. For example, if a company has a large corpus of reliable information about its products or daily operations, a RAG architecture will provide that data to augment the prompts and responses that go through the LLM, making outputs more useful, verifiable, and precise. This can improve help desk automation, product availability checks in retail, or even healthcare as doctors’ notes can be quickly made available for patients or other clinicians.

RAG Use Cases

Common benefits of RAG across industries include better and more complete data retrieval, enhanced customer support, and the ability to generate personalized content. By supplementing LLMs with current information, organizations can deploy AI agents to provide real-time and contextually relevant answers to user queries, minimizing the need for human intervention. The versatility of RAG allows it to adapt to a wide range of applications, including the following:

• Business intelligence. Companies can use RAG to help GenAI models pull relevant market data for automated production of insights and reports. This data can include market research, competitor analysis, sales volume, and customer feedback.
• Content recommendations. RAG can improve content recommendation systems, often in tandem with vector databases. RAG enables the AI model to retrieve and analyze the user reviews, ratings, and content descriptions that allow the system to generate personalized recommendations aligned with the user’s prompt.
• Fact-checking in journalism and other media. RAG can help organizations quickly retrieve and cross-reference documents to verify claims made in articles, reports, or social media.
• Medical diagnosis. In healthcare, RAG can be applied to support doctors and other clinicians by aiding in diagnosis and treatment planning. It does this by helping AI models retrieve medical case studies, research papers, or clinical trial results that are relevant to a patient’s symptoms or condition.
• Technical support. RAG can be used to help reduce resolution time and improve user satisfaction in tech support. The system might retrieve relevant troubleshooting guides and documentation or even scan forum threads and provide those to the LLM to help resolve user issues. In addition, RAG can reference a database that contains records of a customer’s recent interactions for more customized and personal service.

What Is Fine-Tuning?

Fine-tuning a generative AI model means taking a general-purpose model, such as Claude 2 from Anthropic, Command from Cohere, or Llama 2 from Meta; giving it additional rounds of training on a smaller, domain-specific data set; and adjusting the model’s parameters based on this training. This tuning helps the model perform better on specific tasks because it’s been adapted to the nuances and terminology of a particular domain, such as coding or healthcare.

When to Use Fine-Tuning

Choose fine-tuning when an LLM needs to be deft in a particular domain. With extra training, an LLM can better understand prompts and deliver outputs that reflect the nuances and terminology of a particular field. You’ll need access to a large data set or storehouse of documents curated for the training process, but fine-tuning is worth the effort because it allows for greater control over the style, tone, and manner of generated content. That can pay off in your marketing materials or customer interactions. Fine-tuning, like RAG, can also be helpful in medicine, coding, and other highly specialized domains.

Fine-Tuning Use Cases

Fine-tuning, the process of adapting a general AI model to a specific task or domain, is a powerful technique that can significantly improve results for a range of organizations, especially in cases where personalization and specialization are key. Here are some common use cases where it can be particularly effective:

• Customer support automation. Fine-tuning an LLM using a large, well-curated collection of data and documents about your company’s products, services, and operations can help an LLM be a more useful automated customer support system. The fine-tuned LLM will better understand the vocabulary and nuances in customer interactions and be able to respond appropriately.
• Educational content. LLMs can be fine-tuned on educational materials in a specific domain, such as history or grammar. The LLM can then help create new learning content, summarize textbooks, generate quiz questions, even provide tutoring sessions in various subject areas.
• Medical information processing. LLMs can be fine-tuned with medical literature, anonymized patient records, and other medical texts and images, making them more useful for suggesting treatments and diagnostics.

Retrieval-Augmented Generation (RAG) vs. Fine-Tuning: Key Differences

Both fine-tuning and RAG make general-purpose LLMs more useful, but they do it in different ways. A simple analogy is that fine-tuning an LLM gives it a deeper understanding of a particular domain, such as medicine or education, while pairing the LLM with a RAG architecture gives it access to up-to-date, local data for its responses.

Why not use them together to get responses that are both nuanced and timely? It’s a growing trend and even comes with its own acronym: RAFT, for retrieval-augmented fine-tuning. With this hybrid approach, a model fine-tuned on specialized domain data is then deployed in a RAG architecture, where it uses its domain expertise to retrieve the most relevant information during response generation. The result is highly accurate, relevant, and context-aware outputs.

We’ll discuss RAFT further in a bit, but first let’s get a better understanding of the two approaches.

Fine Tuning

Both RAG and fine-tuning help an LLM move beyond generic responses drawn from its original, generalized training data sets. Fine-tuning involves putting an LLM through extra rounds of training using data sets that are specific to a particular domain or organization.

• Requirements
  That requires IT and business teams to put in the upfront work to gather, clean, and label large data sets for these new rounds of training. The training regime itself is compute-intensive, requiring an advanced AI architecture of neural networks backed by enough GPUs to train the LLM in a reasonable amount of time.
• Outcome
  The result is an LLM that’s fluent in the information and parlance of a particular domain or business case.
• Potential downsides
  Unlike a RAG system, the LLM is entirely reliant on the data set used for its fine-tuning training regime and lacks access to updated external knowledge. A fine-tuned LLM can also lose, or “forget,” some of the finer points of its original training. For example, it might lose finesse in general conversation as it becomes immersed in a particular specialty, such as medicine. Perhaps you’ve known doctors who suffer this same fate.

RAG

RAG also alters the responses of LLMs, but it doesn’t change the underlying model. Instead, a RAG system uses a local database or curated collection of documents to inform an LLM’s responses, often with up-to-the-minute details.

• Strengths
  The RAG architecture is considered superior to fine-tuning in terms of data security and privacy because data can be stored in a secured environment with strict access controls, helping ensure that private data isn’t reflected in AI responses.
• Weaknesses
  A weakness of this approach compared with fine-tuning is that the language models aren’t trained for accuracy in any particular domain; they’re working from the general knowledge of the LLM’s training.

Comparison of Skill Sets and Costs

• Skill Sets
  In terms of skill sets, while RAG is simpler to implement, RAG and fine-tuning require overlapping expertise in coding and data management. Beyond that, however, a team involved in fine-tuning needs more expertise in natural language processing (NLP), deep learning, and model configuration.
• Time and Cost
  Fine-tuning requires more upfront work while RAG requires more resources at runtime. Fine-tuning means rounds of compute-intensive training before the LLM can be deployed, making it a more expensive project versus a RAG architecture. Once a fine-tuned LLM is put into service, however, the runtime architecture is fairly straightforward. At this point, a RAG system adds an additional layer of complexity to the LLM, requiring a team to maintain an up-to-date database and additional computational resources for each prompt.

Hybrid Approach: RAFT

The limitations—and benefits—of these two approaches have, quite naturally, led to a growing trend to combine their strengths. The result is the hybrid approach called RAFT.

How to Choose Between RAG and Fine-Tuning

The choice between using a RAG architecture or a fine-tuning regime comes down to the resources you have and how you’ll use your LLM. As noted in the table below, most use cases will benefit from the effort to combine the two approaches—for most companies, once they’ve put in the effort to fine-tune, RAG is a natural addition. But here are six questions to ask to determine which to prioritize:

1. Must responses include local and very current data? Informing LLM responses with your own up-to-date data is a strength of RAG and why it has quickly gained popularity.
2. Is the LLM work in a specialized industry? Fine-tuning allows an LLM to better interpret prompts and deliver responses in the unique language of a particular task or field of operations, such as healthcare.
3. Are data privacy and security paramount? A RAG architecture allows an organization to keep sensitive data in a well-secured, local database.
4. Are tone and manner of responses important? Fine-tuning allows an LLM to offer responses in the specialized language preferred by an organization or a particular field. If clients, retail customers, or partners are going to query the LLM, fine-tuning adds a professional tone.
5. Are runtime resources limited? A fine-tuned LLM requires no more runtime resources than a general-purpose LLM. RAG is more complex, requiring the LLM to query local databases to augment responses. That adds overhead.
6. Is there access to compute infrastructure and AI skill sets? Fine-tuning an LLM requires both. RAG needs runtime resources and data infrastructure but fewer AI skills.

Get More Business Value from GenAI with Oracle Cloud Infrastructure

Whether you choose RAG or fine-tuning or both, Oracle specializes in helping organizations like yours make productivity gains with Oracle Cloud Infrastructure (OCI) Generative AI, a fully managed service that includes the power of OCI and a choice of open source or proprietary LLMs.

We make it easy to combine your LLM with RAG so you can get up-to-date responses grounded in your diverse knowledge bases. When it’s time to run your fine-tuning regime, Oracle AI infrastructure is a great choice. You’ll find superclusters that scale up to 65,536 GPUs—more than enough to run your most demanding training and inference workloads, such as LLM responses, computer vision, and predictive analytics.

General-purpose LLMs continue to improve, with a constant flow of new versions arriving from the likes of Anthropic, Cohere, Google, Meta, and many others. But no matter how deftly these AI models handle human language, they will always need a way to connect that skill set to the specific needs of business use cases. Fine-tuning and RAG are currently the two best methods for doing this. Look for them to continue evolving as AI models, hardware, and data architectures advance.

RAG vs. Fine-Tuning FAQs

Is RAG better than fine-tuning?

RAG and AI model fine-tuning are different, with their own benefits and costs. Both are popular methods of making generative AI models more useful, and each organization should choose the method that best fits its needs. Another popular option is to combine the two approaches, called RAFT, for retrieval-augmented fine-tuning.

What’s better than RAG?

RAG is simply a technique for helping an LLM deliver better responses by referencing a company’s data and documents. A method called GraphRAG has emerged as a way to further enhance LLM responses beyond what a RAG architecture can do on its own, but it adds architectural complexity and popular use cases have yet to emerge.

Fine-tuning an AI model is another method that can help an LLM offer more targeted or nuanced responses, and it can be combined with RAG to further improve the LLM’s performance.

Can RAG and fine-tuning be used together?

Yes. This hybrid approach offers a model fine-tuned on specialized domain data, then deployed in a RAG architecture so it can offer the latest or most relevant information in its responses.

What’s the difference between RAG and transfer learning?

RAG improves an LLM’s responses by accessing a local, up-to-date knowledge base. Transfer learning improves the responses of a general purpose AI model by accessing a separate AI model that has been fine-tuned to work in a particular domain.