What is the difference between RAG and fine-tuning? A comparison and explanation of how to use LLM accuracy improvement methods

07/24/2024

08/24/2026

What is the difference between RAG and fine-tuning? A comparison and explanation of how to use LLM accuracy improvement methods

LLMs (Large Language Models) like ChatGPT and Gemini excel at performing a wide range of tasks such as document generation, programming, and information retrieval with high accuracy in response to user prompts that resemble natural speech. However, it is important to note that the generated content is not always guaranteed to be correct. The data that LLMs learn from consists of text data available on the internet, but they do not always learn the most up-to-date information. Additionally, they cannot learn information that is not available online. Due to their nature, LLMs can produce responses that sound plausible even when they are based on incorrect information, a phenomenon known as "hallucination."

In order to promote DX in business, we want to utilize LLMs to improve efficiency and productivity in various operations. However, due to the challenges mentioned above, it is difficult to use existing LLMs as they are. That said, developing an LLM from scratch incurs enormous development costs, making it unrealistic. Therefore, various technologies are being developed to customize existing LLMs and enhance their accuracy. Among these, RAG and fine-tuning are attracting attention.

This time, we will explain the differences between RAG and fine-tuning for customizing LLMs.

Table of Contents

1. What is RAG? A Simple Explanation of Its Mechanism, Advantages, and Disadvantages
2. What is Fine-Tuning? Explanation of Its Mechanism, Formats, and Characteristics
3. What Are the Differences Between RAG and Fine-Tuning? Easy-to-Understand Explanation with a Comparison Table
4. Which Should You Choose? A Usage Guide by Use Case
5. Human Science Annotation, LLM RAG Data Structuring Agency Service

1. What is RAG? A Simple Explanation of Its Mechanism, Advantages, and Disadvantages

As we have explained in our company blog, RAG stands for Retrieval-Augmented Generation. The mechanism of RAG can be simply described as the LLM searching for information from untrained data (external data) based on the user's input prompt, adding the search results to the prompt, and passing it to the LLM. The LLM generates responses based on this information. A key feature of RAG is that there is no need for additional training of the LLM itself.

Reference Blog

>What Are LLM and RAG for Improving Business Efficiency? Explaining the Business Use of Generative AI

●Benefits

・Enables generation of responses based on the latest information
RAG searches information in a dedicated RAG database that stores external data. If the information in this database is updated with the latest data, the LLM’s responses can always be generated covering the most current information.

●Disadvantages

・The accuracy of the answers depends on the precision of the external data
If there are errors in the database information, that incorrect information will be passed to the LLM for generation. As a result, correct answers cannot be obtained.

・Data maintenance is necessary
To perform accurate searches with RAG, attention must be paid to the condition of the data placed in the database. For example, parts of PDF documents where images are embedded cannot be searched by RAG. It is important to use the original text-based Word files before they were converted to images if available. Additionally, structuring documents as much as possible and ensuring that if multiple versions of a document exist, the latest version is used, are essential for maintaining data that allows RAG to perform accurate searches.

2. What is Fine-Tuning? Explanation of Its Mechanism, Formats, and Characteristics

Fine-tuning is a method of customizing existing LLMs by providing additional training datasets tailored to specific purposes. Fine-tuning itself has been used in deep learning, which mimics neural networks, even before the advent of LLMs. The mechanism involves adding a new layer to the output layer of the neural network and adjusting the parameters across the entire LLM through training with the dataset.

The format of the training dataset varies by LLM. For example, for ChatGPT, the dataset is described in a format such as JSONL with <prompt (question), completion (answer)>.

●Benefits

・Improved accuracy for specific domains and tasks
Fine-tuning, by its mechanism of having the LLM itself learn, allows for training specialized in specific domains. For example, by training specialized in fields such as medical care, it becomes possible to operate a more accurate LLM.

●Disadvantages

・Creation of a large dataset is necessary
The dataset needs to be in a format that the LLM can learn from. It is necessary to create such a dataset, and although the exact amount varies, in some cases thousands or tens of thousands of data points must be prepared manually, which requires an enormous amount of labor.

3. What Are the Differences Between RAG and Fine-Tuning? Easy-to-Understand Explanation with a Comparison Table

Both methods share the commonality of customizing the LLM using external data. The major difference is whether the LLM itself undergoes additional training. In RAG, external data is searched and passed to the existing LLM, so there is no need for additional training. On the other hand, in fine-tuning, the LLM undergoes additional training with external data, resulting in the LLM itself being customized.

The characteristics mentioned so far are summarized below.

	RAG	Fine-tuning
LLM Learning	None	Available
Training Data	・Documents stored in the database, etc.	・Formats that LLM can process
Response Generation Method	・Search the database and generate answers using existing LLM	・The LLM itself generates responses through additional training
Benefits	- Easy updates for the latest information	・High accuracy of responses for specific domains and tasks
Disadvantages	・If misinformation is included, accuracy cannot be achieved ・Data organization is necessary	- Creation of training datasets is necessary
Main Uses	・When you want to refer to and respond based on frequently updated information such as internal documents, FAQs, manuals, news, and regulations	・When you want the model to learn expressions and advanced expertise in specific fields such as medical, legal, manufacturing, and finance
Cost	・Since retraining the LLM itself is unnecessary, it is relatively low-cost and easy to implement ・Organizing and structuring the searchable data requires significant effort	・Because creating, validating, and retraining the training dataset incurs costs, the initial investment tends to be large
Implementation Period	・If existing documents are organized and stored in the search database, implementation can be completed in a relatively short period	・Since design, creation, and evaluation of training data are required, the implementation period tends to be longer compared to RAG
Accuracy Characteristics	・Depends on the quality of the external data referenced and the search accuracy ・While it is easy to reflect the latest information, insufficient data organization leads to decreased answer accuracy	・Improvement in consistency and accuracy of responses for specific tasks and specialized fields can be expected ・Retraining or additional measures are necessary to reflect the latest information

There are such differences between RAG and fine-tuning. It cannot be said outright which method is superior. While RAG is suitable for handling the latest information, fine-tuning is more appropriate if you seek deeper accuracy in specific fields of expertise. Therefore, which method is suitable depends on the purpose, so it is important to carefully consider and choose the customization method.

4. Which Should You Choose? A Usage Guide by Use Case

When choosing between RAG and fine-tuning, it is easier to organize your thoughts by first considering whether you want to "reference information externally" or "change the model's behavior itself." For information that is frequently updated, such as internal documents, FAQs, regulations, and news, RAG is suitable because it easily reflects the latest information simply by updating the database. On the other hand, if you want to improve the accuracy of responses in specialized fields or stabilize a specific writing style, judgment criteria, or output format, fine-tuning is effective. However, the quality of data affects the results in either method. It is also effective to first establish an information referencing mechanism with RAG and combine fine-tuning as needed.

Comparison of RAG and Fine-tuning

Perspective	RAG	Fine-tuning
Which should be used?	Suitable for cases handling the latest information. Such as news articles and internal knowledge searches.	Effective when you want to deeply address specialized fields. Such as medical, legal, and manufacturing.
Examples of learning formats	Vectorize PDFs, Word documents, and FAQs and store them in a search database.	Prepare a large number of "question-answer" pairs in JSONL format, etc., for additional training.
Cost and Time Comparison	No retraining required, enabling short-term and low-cost implementation.	Data creation and training require significant effort, resulting in high initial investment and longer duration.
Trends Among Implementing Companies	Areas handling the latest information such as media companies and customer support.	Highly specialized fields such as medical, legal, and manufacturing. Hybrid implementations with RAG are also increasing.

RAG and fine-tuning, as methods for customizing LLMs, each have their own strengths and challenges. While RAG enables speedy and low-cost utilization of the latest information, fine-tuning requires large-scale data preparation but significantly improves answer accuracy in specialized fields. In either case, properly preparing the quality and quantity of external data is the key to success. It is advisable to choose the optimal method according to your company's goals and resources, and sometimes consider a hybrid implementation combining RAG and fine-tuning.

5. Human Science Annotation, LLM RAG Data Structuring Agency Service

Over 48 million pieces of training data created

At Human Science, we are involved in AI model development projects across various industries, starting with natural language processing and extending to medical support, automotive, IT, manufacturing, and construction, just to name a few. Through direct business with many companies, including GAFAM, we have provided over 48 million pieces of high-quality training data. No matter the industry, our team of 150 annotators is prepared to accommodate various types of annotation, data labeling, and data structuring, from small-scale projects to big long-term projects.

Resource management without crowdsourcing

At Human Science, we do not use crowdsourcing. Instead, projects are handled by personnel who are contracted with us directly. Based on a solid understanding of each member's practical experience and their evaluations from previous projects, we form teams that can deliver maximum performance.

Support for not just annotation, but the creation and structuring of generative AI LLM datasets

In addition to labeling for data organization and annotation for identification-based AI systems, Human Science also supports the structuring of document data for generative AI and LLM RAG construction. Since our founding, our primary business has been in manual production, and we can leverage our deep knowledge of various document structures to provide you with optimal solutions.

Secure room available on-site

Within our Shinjuku office at Human Science, we have secure rooms that meet ISMS standards. Therefore, we can guarantee security, even for projects that include highly confidential data. We consider the preservation of confidentiality to be extremely important for all projects. When working remotely as well, our information security management system has received high praise from clients, because not only do we implement hardware measures, we continuously provide security training to our personnel.

In-house Support

We provide staffing services for annotation-experienced personnel and project managers tailored to your tasks and situation. It is also possible to organize a team stationed at your site. Additionally, we support the training of your operators and project managers, assist in selecting tools suited to your circumstances, and help build optimal processes such as automation and work methods to improve quality and productivity. We are here to support your challenges related to annotation and data labeling.