In recent years, the development of LLMs, starting with the GPT series, has been remarkable, dramatically expanding their scope of application from flexible text generation like human conversation to the creation of specialized papers and reports.
On the other hand, the method of providing up-to-date and accurate information while efficiently utilizing vast amounts of data has been raised as a challenge. In this context, "RAG (Retrieval-Augmented Generation)" is attracting attention.
However, many people may have questions such as "How exactly does it work?" or "How can it be used in business?"
Therefore, in this article, we will provide an easy-to-understand explanation of the basic mechanism of RAG, the benefits of its utilization, the implementation process, and the latest cases.
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【Table of Contents】 |
RAG is a technology that combines the generation capabilities of LLMs, which are generative AIs, with the information retrieval technology of external databases.
Conventional generative AI has a fatal challenge called hallucination, where it generates false information. A major feature of RAG is that it is designed to solve this hallucination issue in LLMs.
Specifically, it works by searching for necessary information within designated external sources, and based on that retrieved information, the LLM generates an answer. This mechanism allows the source of the answer to be shown and ensures that the generated answer is based on "facts" backed by information.
RAG generates answers through the following two processes.
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First, in the retrieval process, up-to-date and highly reliable information is quickly acquired from document databases, the Web, knowledge bases, etc., based on the user's question or input content.
Next, in the generation process, an answer is generated based on the acquired information. In the generation process, not only the information retrieval results but also the user's input are passed to the LLM. Therefore, it is possible to provide specific and accurate answers that better match user needs, which was difficult with conventional search systems.
RAG is often compared with "fine-tuning." Fine-tuning is, in short, a technology to adjust the parameters of an AI model. While both are technologies useful for improving the accuracy of generative AI, their characteristics such as flexibility and degree of hallucination differ significantly.
Let's compare their respective features in the table below.
| RAG | Fine-tuning | |
| Overview | A mechanism where information is retrieved from an external database and the AI generates an answer based on that content. | A method of re-training an AI model using new data to optimize it for a specific task or domain. |
| Flexibility | Supports up-to-date information simply by updating the database. | Re-training is required every time to specialize in a new task or domain, resulting in low flexibility. |
| Hallucinations | Easier to reduce factual errors as it answers based on retrieval results. | Hallucinations are likely to occur if the training data is incomplete. |
| Main Usage Scenes | Tasks where knowledge is frequently updated or situations requiring retrieval of specialized information. | Situations where answers completely optimized for company-specific tasks or specific fields are required. |
From the table above, the major difference between RAG and fine-tuning is whether re-training is necessary for information acquisition. When utilizing each, it is good to make a selection based on these differences, according to the application and the necessity of information updates.
The benefits of RAG will be introduced while comparing it with LLMs and conventional search systems.
LLMs generate answers based on training data. Therefore, if corporate confidential information is included in the training data, there is a concern regarding the risk of data being unintentionally leaked externally.
On the other hand, RAG is a mechanism that does not depend on training data and dynamically acquires information from internal databases. Therefore, the risk of confidential information leaking externally can be minimized.
Highly sensitive data, which was previously difficult to utilize due to information leakage risks becoming a bottleneck, can be used safely, significantly expanding the scope of generative AI applications.
RAG is a mechanism where relevant information is searched and collected through a retrieval process, and based on that information, the generative model creates an answer. This mechanism can suppress the occurrence of hallucinations that conventional LLMs tend to fall into, allowing for more accurate and detailed answers.
Therefore, there is a possibility of utilization even in business scenes where highly accurate information is required, such as FAQ systems including specialized questions involving laws like the Pharmaceutical Affairs Law.
Additionally, because RAG generates answers from retrieval results of external data sources such as Web information and specialized documents, it can provide answers that always reflect the latest information.
When LLMs or conventional search systems are used for internal FAQs, high-frequency data updates are necessary. Therefore, especially in environments where information changes frequently, the heavy burden on personnel and the time required for system updates become challenges.
On the other hand, RAG can update information simply through database update tasks, and does not require re-training of the model itself like LLMs. Therefore, the costs incurred for data updates can be significantly reduced.
Here, the process of implementing RAG is introduced. To effectively utilize RAG, it is particularly important to perform data processing and environment construction in the initial stages of introduction.
An important process that greatly affects RAG performance is data preprocessing. Data preprocessing refers to the task of organizing and preparing data, and in the case of RAG, the following processes are necessary.
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Through annotation, unstructured data is structured and converted into a format easily understood by AI. Appropriate annotation streamlines the retrieval process of the RAG system, allowing relevant information to be identified quickly.
For details on annotation, please see the article below.
「"What is text annotation? Explaining the types, why it's important in natural language processing, examples of its use, and points to note!"」
For the introduction of RAG, constructing an efficient retrieval system is important. At this stage, a search engine for quickly and accurately acquiring relevant information is selected, and a system capable of high-speed search is introduced.
The retrieval system can be called the foundation of RAG, and because the performance of the retrieval system directly affects answer accuracy and processing speed, it needs to be selected carefully.
The core process of a RAG system is integration with an LLM. The tasks performed in this process are as follows.
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First, a model is selected from among many LLMs such as the GPT series and Claude, and linked with the retrieval system. By choosing a model suitable for the task during selection, it is possible to improve answer accuracy.
After introduction, testing and evaluation are necessary to confirm whether the RAG system is operating appropriately.
First, in testing, the system's operation is confirmed using actual input data to identify error occurrence conditions or situations where no answer is given. By assuming realistic utilization scenarios and inputting diverse questions or inquiries, it is evaluated whether the system can accurately retrieve information and generate appropriate answers.
Then, the accuracy of answers, search speed, and user satisfaction are evaluated, and adjustments are made if there are improvement points.
The number of companies achieving operational efficiency and productivity improvements by introducing RAG is increasing. Here, use cases of RAG are introduced.
At Inaba Seisakusho Co., Ltd., inquiries from business partners were increasing and diversifying along with the increase in product lineups and sales offices. Especially in sales settings, because there was a need to respond instantly to questions from customers, a situation occurred where sales employees telephoned the development department each time, placing an operational burden on both the sales and development departments.
To solve these challenges, they introduced "OfficeBot," a RAG system for enterprises developed by Neos Corporation.
After introduction, sales employees became able to access OfficeBot from smartphones even while out, and obtain rapid answers to questions from customers. As a result, the number of phone calls per sales employee has decreased, and they have succeeded in operational efficiency.
At Ajinomoto Frozen Foods Co., Inc., against the background of increasing use cases of generative AI in the food industry, they were considering ways to use ChatGPT in a highly secure environment. At the same time, the increasing need to efficiently utilize the vast amounts of data accumulated internally also pushed the internal utilization of ChatGPT forward.
Ajinomoto Frozen Foods constructed a system aimed at supporting new employees using "ChatSense," a RAG system by KnowledgeSense Inc. that allows AI utilization in a newly introduced secure environment.
As a result, know-how accumulated internally was effectively unearthed, and they succeeded in improving productivity.
Examples of points to consider and countermeasures when implementing RAG are introduced.
The accuracy of RAG depends heavily on the quality of the database used. For example, if old manuals or incomplete documents are included in the search target, inappropriate answers may be generated.
Therefore, when introducing RAG, data preprocessing is necessary as a prior preparation.
In particular, annotation is a very important task in improving RAG accuracy. By attaching appropriate labels to data, search accuracy improves, and relevant information can be reached quickly.
However, because annotation requires a lot of time and effort, if it is difficult to handle with only internal resources, external outsourcing to a specialized annotation company is effective. By leaving it to a specialized company, the burden on internal resources can be reduced while ensuring data quality.
Because RAG undergoes a two-stage process of retrieval and generation, processing takes time.
Therefore, in situations where users expect real-time performance, such as applications like chatbots where immediate response is required, response latency can become a stressor and cause a decline in user experience.
Especially for search queries targeting large amounts of data, the retrieval process takes time, and immediate answers may become difficult in some cases, so countermeasures such as optimization of the retrieval system and optimization of the generative AI are necessary.
Through these countermeasures, smoother responses can be expected.
Because RAG utilizes external data and corporate internal confidential data, it is accompanied by risks of information leakage and unauthorized access.
Especially when using a cloud-based system, the risk of leakage of corporate confidential information increases if security measures are insufficient. It is necessary to implement measures such as data encryption and access control.
RAG is a tool that advances the utilization of generative AI in business scenes by generating accurate answers through source retrieval.
It is becoming an indispensable technology as a means to solve the hallucination issue held by LLMs. Therefore, as the introduction of generative AI currently progresses rapidly, its utilization is spreading in diverse applications such as customer support, IT support, and internal FAQs.
Among companies that have actually introduced it, not a few have succeeded in operational efficiency and productivity improvement as the flexibility and high answer accuracy of RAG match their business.
However, to increase the introduction effect of RAG, accurate preprocessing including text annotation is important. By preparing high-quality data, let's improve the accuracy of RAG and lead the introduction to success.