Retrieval Augmented Generation (RAG) vs Fine-Tuning
Retrieval augmented generation (RAG) and fine-tuning are two of the key techniques that we can use to improve the output of AI-powered tools within a specific domain.
However, they are far from interchangeable. Indeed, each one is a distinct technique, with its own use cases, benefits, challenges, and requirements.
Today, we’re exploring what each one is, where they differ, and how we can leverage them within our own development projects.
Specifically, we’ll be covering:
We’ll start with the basics.
What is retrieval augmented generation (RAG)?
Retrieval augmented generation means enabling LLMs to utilize information from external data sources when they generate responses. The idea is to provide models with up-to-date, accurate, or domain-specific information.
In turn, this enables them to generate more accurate, relevant, and contextually appropriate answers.
To understand the importance of this, we can briefly understand how LLMs typically generate answers in response to prompts.
LLMs are trained on massive data sets. These allow them to use patterns in natural language to interpret inputs and determine the appropriate information to respond with. However, this approach is limited by the training data, including in terms of the cut-off date for what’s included.
So, RAG can be used to furnish models, and ultimately end users, with more recent information, as well as providing citations for the content of their responses. We’ll return to some more concrete benefits of this in a few moments.
How does RAG work?
Knowing what RAG is at a high level, it’s helpful to consider how this works in more technical detail.
RAG systems connect LLMs to external data sources via what’s known as a vector database. This stores numerical representations of external knowledge sources, making it easier for the model to search and interpret information.
RAG solutions are often made up of the following four elements:
- Embedding model - Used to vectorize documents for processing by LLMs.
- Retriever - To process questions and return the most relevant document vectors.
- Reranker - Optionally re-evaluating the relevance of the retrieved documents.
- Language model - Taking the top documents and creating an appropriate response for the original question.
So, in the simplest examples, a user submits a query, the system searches connected documents, determines which ones are relevant, and uses this information alongside its training data in order to generate a response.
However, it’s important to note that different configurations of this process are also possible.
For instance, some RAG solutions utilize more advanced tools such as sub-queries or hypothetical document embeddings to increase accuracy and validity within the retrieval process.
Benefits
Earlier, we said that RAG aims to enable AI-powered systems to return more accurate, up-to-date, and relevant information for end users. To better understand this, we can outline the more granular technical and business-level benefits of retrieval augmented generation.
With this in mind, the core benefits of RAG include:
- Improved accuracy and reduced hallucinations - By grounding responses in real-world business information.
- Relevance and context - Providing more contextually relevant information to end users.
- Up-to-date knowledge - Allowing LLMs to access information as and when it’s updated.
- Cost effectiveness - RAG is typically more cost-effective than continuously retraining models.
- Transparency and traceability - Providing us with greater insights into where LLMs are pulling information from.
- Adaptability - RAG is domain agnostic, meaning we can create solutions for a variety of industries, without specialized models.
- Ease of maintenance - Reducing the maintenance burden as we don’t need to continuously retrain models.
- Scalability - RAG solutions can handle large volumes of data, making them relatively easy to scale.
We’ll return to some of the key use cases that this makes RAG well-suited for a little later.
For now, the key thing to understand is that RAG offers us an effective means to utilize real business documents and information within AI-generated responses, helping to enable more effective generative solutions.
Take a look at our guide to enterprise chatbots.
Challenges
However, none of the benefits we just outlined can be taken for granted.
Instead, we must also be aware of key challenges and limitations that we’re likely to encounter when implementing RAG systems.
These include:
- Data issues - Issues arising from noisy, incomplete, or inaccurate information.
- Contextual misalignment - Unreliable outputs related to poor data matching with user intents.
- Complex document structures - Some RAG solutions may struggle with more complex document structures.
- Computational costs - RAG solutions can require a relatively high computational overhead.
- Security concerns - Including exposing LLMs to sensitive information.
- Domain specificity - RAG systems can struggle with the nuances of different industry-specific use cases.
- Handling structured data - Queries involving tabular or list data can present challenges for retrieval augmented generation.
- Fallback models - Implementing fallback mechanisms in order to deal with errors and maintain integrity.
While not deal-breakers, some of these may make other techniques more suitable for particular use cases. We’ll return to the situations that RAG is best suited for a little later.
First, though, we can move on to checking out a different technique, fine-tuning.
What is fine-tuning?
Fine-tuning is a machine learning technique that involves providing a pre-trained model with further training data that is relevant to a particular task or domain.
The idea is to adapt an existing model to be more effective for a particular use case or industry. So, we build on the basic capabilities of the existing model with further domain-specific training data.
Like RAG, one high-level goal of this is to empower LLM-powered applications to provide more relevant, accurate information in response to prompts.
However, the core challenge that this solves is a little bit different compared to RAG. That is, fine-tuning is primarily focused on improving the effectiveness of the model itself at handling specific use cases, rather than exposing it to additional external, domain-specific information.
In other words, we’re still providing the LLM with additional information, but we’re doing it by expanding the training data, not by connecting the model to external sources.
Therefore, fine-tuning is an effective method to provide AI-powered systems with domain-specific knowledge and understanding, making them more suited for granular or niche tasks, compared to off-the-shelf models.
How does fine-tuning work?
Knowing what fine-tuning is, we can think more deeply about how it works and how it can be implemented.
As you might expect, fine-tuning requires us to select an appropriate model in the first place. That is, fine-tuning will be easier when we start with a model that’s already relatively well-suited to our needs.
To begin fine-tuning this, we’ll need to prepare a task-specific data set. This is a curated data set that contains information and examples that are relevant to the specific task or use case the model is being fine-tuned for.
This data is then used to adjust the weights and parameters of the model using what’s known as supervised learning. This means utilizing labeled data, providing example inputs and their corresponding outputs.
Fine-tuning in this way is generally conducted as an iterative process, repeatedly refining and improving the model’s performance and understanding of the task.
As we’ll see shortly, this is a valuable tool when creating solutions for a range of use cases, where more domain-specific understanding is required from LLMs.
Benefits
Beyond the top-level benefit of expanding a model’s training data with domain-specific information, we can now drill into more detailed benefits of fine-tuning, including both technical and business-level aspects, just as we did earlier with RAG.
These include:
- Task-specific performance - By training a model on task-specific, we can enhance it’s ability to understand the requirements of the use case at hand.
- Domain adaptations - Including the nuances of domain-specific language and industry jargon.
- Efficiency - Potentially eliminating the need for calls to external data sources, as well as enabling us to utilize smaller language models.
- Reduced hallucinations - Reducing the occurrence of errors and misunderstandings in LLM-powered systems.
- Addressing fringe cases - Fine-tuned models may be more adept at handling rare or niche scenarios.
- Cost and resource savings - Fine-tuning a pretrained model is much more cost and resource-efficient than creating a proprietary model from scratch.
Again, we’ll see how some of these can be applied at a practical level when we come to think about use cases for RAG vs fine-tuning.
Before we get to that, though, it’s important to consider some of the limitations and challenges we might encounter when fine-tuning a model.
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Fine-tuning challenges
Fine-tuning can be a difficult process to get right, including in terms of acquiring and using the relevant data.
Here are some of the key considerations we’ll need to watch out for:
- Overfitting - Where models become overly specialized.
- Catastrophic forgetting - Fine-tuning may cause models to forget previously learned general knowledge.
- Fine-tuning data becomes out of date - Fine-tuning may not be the most effective method to expose models to information that must be regularly updated.
- Bias amplification - Fine-tuning may amplify biases in existing training data.
- Data requirements - The data required to fine-tune models may be unreliable or difficult to acquire.
- Computational resources - Fine-tuning LLMs can be a relatively computationally intensive process.
As with RAG, however, none of these are necessarily critical issues. Instead, what they illustrate is the importance of understanding when fine-tuning is an appropriate option, and when we might want to consider alternative approaches.
Use cases
So, our next task is to consider the respective use cases for RAG vs fine-tuning. While we explore some of the most common examples, it’s important to keep some caveats in mind.
This largely comes down to the fact that the two aren’t mutually exclusive. So, there are some instances where either option might be viable, as well as use cases where a combination of both techniques would be most effective.
Let’s check out the key use cases for each one in turn.
RAG
As we know, RAG is primarily used to enable LLM-powered systems to interact with external information sources, in order to provide more accurate, relevant answers to end users.
However, we also know that this can introduce challenges, including comparatively high computational requirements. With that in mind, we must understand the specific kinds of applications where RAG can add value.
These include use cases where it’s particularly important to provide detailed, up-to-date information.
Common examples include:
- Enterprise chatbots - Internal assistants that can answer queries on policies and procedures across the enterprise.
- Internal services workflows - For example, ITSM or HR workflows, where access to up-to-date information is critical.
- Customer service assistants - Using relevant information to help field questions from customers.
- AI agents - Providing agentic systems with relevant contextual information to help them reason and take action.
- Document-centric workflows - including retrieving information from contracts, invoices, or other documents.
Of course, these are just a few examples of the kinds of tools that rely heavily on retrieval augmented generation.
In truth, though, RAG is used for a large and growing set of use cases, across all kinds of industries and niches.
Fine-tuning
Earlier, we said that fine-tuning is primarily relied on for adapting existing models to make them more effective for use cases requiring a higher level of domain specificity.
Another way of thinking about this is that fine-tuning is used in situations where we need our AI systems to have specialist knowledge, beyond the more general coverage of their training data.
As you might expect, these largely correspond to the kinds of human job functions that typically require specialist knowledge. This includes departments such as engineering, IT, finance, HR, and legal.
In terms of more specific solutions, this can be helpful in a range of situations across departments, including:
- Text summarizations,
- Translation,
- Internal chatbots,
- Customizing style,
- Incorporating internal data,
- Ensuring reliability in mission-critical domains,
- Handling complex prompts,
- Improving the performance of RAG systems,
- Dealing with a large number of examples that exceed the model’s context window.
So, unlike RAG, where we can point to more specific types of systems that we can build, fine-tuning is more generally utilized across different types of LLM-powered solutions, based on their underlying knowledge needs.
RAG vs fine-tuning: key recommendations
To wrap up, we can highlight some of the key points about what we’ve learned so far, as well as the key points underlying how and when we can use each technique for improving the performance of LLM-powered applications.
At a high level, both involve furnishing AI systems with additional data and information to improve their performance and reliability on specific tasks.
The key difference between them is that RAG seeks to enable models to interact with external data when generating responses, while fine-tuning provides new information to the model itself, as additional training data.
As such, each one presents its own unique benefits, challenges, and ideal use cases.
One important thing to note, however, is that the two are not mutually exclusive. Indeed, while there are cases in which we might implement one method or the other, it’s also common to utilize both in tandem, especially for more advanced solutions.
For instance, we can build powerful, highly effective AI tools by providing models with additional domain-specific training data, as well as exposing them to external information sources for generating responses.
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