RAG vs. Fine-Tuning: Which is Better?

LLMs are a powerful tool for reasoning and language generation. But, since answers rely on statistics and weights, some topics may not have as much training data or reinforcement, leading to the probability of hallucinations.

Beyond this risk, AI models train on public information and are adjusted for general use cases and common knowledge fields. For business use, their outputs are too broad, especially when talking about unique products, internal procedures, or company data. This is when the dilemma between retrieval-augmented generation (RAG) and fine-tuning comes into the discussion.

Considering costs and complexity, the current consensus is that RAG is a much more viable solution than fine-tuning. Let’s walk through each to discover if your company is part of this rule or the exception.

Retrieval-augmented generation (RAG) vs fine-tuning

A RAG system is the preferred approach for most use cases, as it’s easier and faster to implement and more cost-effective. Fine-tuning is better for specialized and niche tasks where the model needs to be highly accurate and consistent.

Now let's dive into the details of each approach.

What is retrieval augmented generation (RAG)?

In a nutshell, RAG is the process of triggering a search in a database to retrieve information relevant to the user’s prompt before sending it to an LLM. For example, if a user asks an onboarding chatbot about the company’s holiday policy, a search gathers the previously uploaded information on that topic. That data is then packed with the user prompt and loaded into the LLM as context. This is the same effect of copying and pasting the content yourself but in a much faster, integrated, and user-friendly way.

Adding this additional context doesn’t change the architecture, layers or parameters of the model. It doesn’t directly adjust the way it calculates outputs (how it “thinks”). It simply provides more information it can use as facts, keeping the calculations close to the terms, topics and concepts of the data present in each request.

On top of this basic functionality, a RAG framework can add a set of perks that increase trust, such as source references for the answer, letting the user click a link and see the original content used to generate the output. At the same time, it can be configured to state that it has no information on the topic: this can be done by adjusting the system instructions of the LLM when the retrieved corpus of data is too small or irrelevant based on the topic.

When we speak of databases, these can be any systems where your data is stored: Sharepoint, Google Drive, Notion or even SQL. As long as you can query the data source and retrieve information based on the prompt, you can create a RAG system. For more efficiency, you can pack your data into a vector database using a platform such as Pinecone, making it better for semantic search and returning the optimal chunks of text required for the answer. This also means you won’t have to use an LLM with a large context window: the RAG framework can be configured to return the right amount of data.

Setting up a system yourself is complex, especially if you don’t have experience working with APIs, database queries and vector representations. But, if you need a solution with less technical complexity, there’s a good selection of no-code options to implement this framework—Stack AI is one of them.

Best use cases for RAG

RAG is best for circumstances where information retrieval is key. For example:

• Creating AI Chatbots or Agents to answer questions based on internal knowledge: customer support AI Chatbots or Agents, employee onboarding, AI sales assistant
• Creating interfaces to interact with high-velocity data, such as live financial market information, supply chain data, or industrial machine data
• Creating tools to search the web or internal knowledge bases and retrieve results based on queries or as fact-checking

With RAG, you can always use the most up-to-date Large Language Model, and feed it with the most relevant information (i.e., internal knowledge of the company). This way you minimize the risk of hallucinations and improve the accuracy of the responses - the model will prioritize the knwoledge that you give as context than the corpus that was used to train it.

What is fine-tuning?

Fine-tuning is the process of training a pre-trained model further to adapt it to a specific task. For example, taking an established model such as GPT-4o and training it with your data set of unique examples. This directly affects the model’s configuration and parameters, changing how it works natively. It expands the base experience with your data, changing how the model calculates the output (or “thinks through” each request).

The additional data you provide to a fine-tuned model can make it better at completing tasks in writing, code generation, sentiment analysis, and more. The keyword here is completing tasks: while fine-tuning may expand the model’s knowledge about certain topics, the main purpose isn’t expanding what it knows. Instead, it’s to improve its reasoning and decision-making abilities in a specific task.

This process is much more efficient than training the model from scratch. You don’t have to design the architecture, prepare numerous labeled datasets, go through each training epoch (covering the associated compute costs), and adjust the model. This is a complex process, requiring expertise in all aspects of machine learning—those skills are rare and expensive in the current market.

Best use cases for fine-tuning

Fine-tuning is best for use cases that involve specializing a model in a set of tasks. You can tune a model with additional examples to:

• Deal with engineering terminology such as “finite element analysis” or “shear stress-strain curves” and document structure, so it can process and generate these kinds of documents
• Adjust a model’s writing output to consistently generate marketing and sales materials that are in tune with brand voice and style guidelines
• Provide a model with additional translation examples so it can more accurately translate text from one language to the other
• Train on code examples unique to your code base so it can generate code according with internal guidelines

When to use RAG or fine-tuning

Cost

When using RAG, there are essentially two cost elements:

• Running the LLM.
• Maintaining the vector database.

Running the LLM involves paying for computing if you’re hosting it on-premises or a cloud hosting solution. If using a third-party API, the pricing is per token. In the latter case, each request using RAG can consume more tokens than usual with the extra context retrieved. Optimizing this involves improving data formatting, implementing tags, and breaking down information into chunks.

Maintaining a vector database is relatively inexpensive. For example, when using Pinecone, you’d have to upload more than 100,000 text-only documents to exceed the free plan. As for retrieving information, 50 employees sending an average of 100 queries per day would cost $8 per month in overages.

Fine-tuning has up-front costs, as you must pay to run the tuning process on your infrastructure (on-premises or cloud). When responding to requests, you’ll still have to cover the costs of running the LLM. However, if you use a third-party API, such as OpenAI’s fine-tuning services, there will be an upfront cost for tuning and a higher cost for input and output tokens when using the tuned model.

Here is the pricing for fine-tuning GPT-4o at the time of writing:

• Each million tokens of training data costs $25.
• Input: $3.75 per million tokens (50% more than base GPT-4o).
• Output: $15 per million tokens (50% more than base GPT-4o).

Skill required

Generative AI platforms on the market such as Stack AI offer no-code methods for building fully-featured RAG systems by anyone, even non-technical people.

On the other hand, fine-tuning requires coding, machine learning, and data science expertise.

Time to value

Use RAG for its lower time to value. Platforms like Stack AI offer a no-code interface, making it easy and fast to configure, manage, and deploy. If your data sources are already organized and ready to upload or connect, you can set it up in less than a day.

Fine-tuning requires careful data preparation procedures (Collect 50,000 to 100,000 labeled examples for your training set), running the tuning job, and testing the model to ensure it meets quality expectations. This process can take weeks or months, depending on the model, task, and the skillset and productivity of the assigned team.

Model variety

RAG is better to leverage new LLMs. You can easily replace the current AI model with newly-released technology. This can be done by changing the API endpoints—or, if you’re a Stack AI user, navigating into the project and changing the model from a dropdown menu.

With fine-tuning, you’re locked to the model that you’ve tuned. Upgrading to a more recent model means starting the tuning process from zero on the new model.

Obsolescence

RAG has a lower risk of obsolescence and is thus better for contexts of high data volatility. As described above, you can replace older models with newer ones. As for the data in the knowledge base, the process is as simple as deleting the old documents and uploading newer ones.

Fine-tuned models are at a higher risk, as the knowledge is baked into the model. As the data changes, you’ll have to retrain the model from scratch to keep it current.

Compute efficiency

Smaller models with fewer parameters cost less to run while providing similar (or higher) performance when fine-tuned. This depends on the amount and quality of data used for tuning the model, making fine-tuning more efficient than RAG from a computing perspective. For example, a case study by Snorkel AI shows that their team fine-tuned a model that performs on the same scale as GPT-3, but is 1400 times smaller and costs 0.1% as much to run.

While edge AI isn’t mainstream yet, more efficient models can be moved from large cloud servers to devices close to where data is generated or actions are taken. This can be used as a cost-optimization strategy, as well as moving cloud models to on-premise machines—with increased data security as a bonus.

Transparency and observability

Use RAG for contexts where transparency is required, since each LLM output can contain the sources and links used to generate the answer. As users verify that the AI responses are consistent with the facts they find in the links, trust in the system will increase.

As for fine-tuned models, it’s still uncertain what’s happening during inference as the parameters calculate the response. Currently, there isn’t a reliable method to open this black box and see how AI is computing each answer.

Latency and throughput

While most use cases don’t require ultra-low latency, fine-tuning is better for systems where completing tasks is time-sensitive. Since the new patterns are part of the model, it doesn’t need to interact with other systems. You can further decrease latency and increase throughput by upgrading your infrastructure—if you’re hosting your model in a cloud solution, reach out to our provider to understand what hardware upgrades you can add to the contract or if there’s a separate infrastructure-as-a-service offering.

While RAG systems are performant for general use cases, as you connect more knowledge bases—and as they contain more data—it will take longer to search for the most contextually relevant data.

RAG and fine-tuning in Stack AI

Stack AI is an enterprise generative AI platform for building tools that optimize and automate your workflows. It offers plenty of tools to set up a RAG system that greatly improves LLM accuracy, with no technical skills required.

It integrates with a wide range of data sources such as Microsoft Sharepoint, Amazon S3 and Google Drive (among many others), with a proprietary search algorithm that surfaces the most relevant information for each user prompt. For long user inputs or when uploading large PDF reports, you can add a dynamic vector store into your project, acting as an AI-friendly database.

You can configure user interfaces directly within the platform, with the option to show sources for each response. There are also methods available to prevent a model from replying if it doesn’t have all the information it needs: you can set these up using a routing node.

As for fine-tuning, there are no ways to tune a model within Stack AI, but it integrates with providers that offer these services: Cerebras, Groq, Replicate or even community models in Hugging Face. Once your fine-tuned model is ready, copy its API key and paste it within your Stack AI project and it’s ready to use. This is also available for in-house models, having a similar integration process.

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