What is LLM・RAG? An explanation of the application of generative AI in business

1. What is LLM?

LLMs (Large Language Models) are AI models built by learning vast amounts of text data through deep learning techniques. Compared to traditional language models, LLMs significantly enhance the three elements of "computational power," "data volume," and "number of parameters." For example, while traditional models have parameter counts ranging from millions to billions, LLMs possess an astonishing number ranging from tens of billions to trillions. High-performance GPUs have enabled the fast processing of these elements, allowing for advanced language understanding and text generation across a wide range of fields.

A representative example of such LLMs is ChatGPT. It has garnered significant attention across various industries since its inception as a model that can respond as if in conversation and generate programming code simply by having users input questions or prompts in spoken language (natural language).

In addition to ChatGPT, various LLMs such as Google LaMDA and GEMINI have been announced and continue to evolve, increasing the potential for their use in business across a wide range of applications.

2. Challenges of LLM

That said, LLMs also have challenges. This is referred to as "hallucination." This phenomenon refers to the generation of text by LLMs that includes incorrect information not based on facts or reality, or completely fictional content. LLM models output words based on the probability of occurrence of text and words. They do not generate text considering whether the information is accurate. They can also generate text about things they have not been trained on.

Causes of 'hallucination' include the LLM not learning the latest information and being unable to access closed data (such as internal data that is isolated from external networks).

To address these challenges, there are techniques such as fine-tuning and additional training of LLMs to enhance their accuracy in specific fields. While these techniques can improve accuracy, they also have the drawback of requiring vast amounts of data and computational resources. A technology that is gaining attention to overcome these drawbacks is RAG.

3. What is RAG?

RAG (Retrieval-Augmented Generation) is a combination of search technology and LLM. While fine-tuning and additional training improve the accuracy of the LLM itself, RAG performs data retrieval and extraction, allowing the LLM to output high-precision answers based on that content. RAG can be operated at a lower cost compared to fine-tuning and additional training, and it also enables access to the latest information and closed data, which was difficult for LLMs. By combining the language understanding capabilities of LLMs with the data retrieval capabilities of RAG for specific areas such as internal information, various business efficiencies can be achieved. The scope of this is extensive. Below are examples of such efficiencies.

Knowledge Management

In knowledge management, it is important to effectively collect, organize, and share knowledge within the organization. With RAG, it is possible to quickly search for relevant information from a large number of documents and databases, allowing for the extraction of necessary knowledge. This enables quick access to the required information, significantly improving operational efficiency. For example, in the manufacturing industry, there may be a vast accumulation of documents such as design standards, processing flow diagrams, business flowcharts, and quality control standards. By utilizing RAG, it becomes possible to quickly search for necessary information within these documents.

Content Generation

It becomes possible to generate content that communicates the company's unique case studies and strengths on websites and blogs. By extracting relevant information through RAG, more accurate information dissemination can be carried out efficiently in a shorter time. For example, when wanting to externally communicate past success stories, utilizing RAG to efficiently collect and integrate various technical reports and staff reports about those cases allows for the generation of content that better highlights the company's strengths.

Customer Support

For manufacturers that release multiple products with manuals spanning hundreds of pages, providing prompt and accurate support in response to customer inquiries is one of the most important tasks. If it takes time to search for the relevant product manual and provide an accurate answer, it can lead to significant losses in customer satisfaction. By utilizing RAG, it becomes possible to analyze past inquiry data and generate optimal responses, which can be expected to improve customer satisfaction. Additionally, it can significantly reduce the burden on the support team and achieve operational efficiency.

4. Challenges of RAG

RAG can search through limited data such as internal data and output results. Conversely, if this data is incorrect, it will produce an incorrect answer, and if the data does not exist, it will not be able to output an answer. Additionally, unstructured data such as scanned blueprints, PDFs, and PowerPoint materials need to be organized to make it easier for RAG to search. For example, for PDFs, methods include applying OCR processing or using Word files that can recognize document layout structures, such as paragraphs before converting to PDF (or creating a Word version).

Therefore, when implementing RAG, it is necessary to maintain and cleanse the data. Additionally, when handling confidential information, security measures such as access restrictions are also essential.

5. Summary

By utilizing LLM and RAG, it is expected to improve efficiency and productivity in various operations. Additionally, it can also be used to support the creation of new value. However, each technology has challenges in terms of accuracy and other aspects. To address these challenges, fine-tuning of LLM or data preparation for improving the accuracy of RAG is necessary. Nevertheless, the task of structuring and organizing the vast amount of documents scattered within the company requires significant labor. Therefore, if it is difficult to allocate internal resources for this, hiring an external vendor is one option.

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

A rich track record of creating 48 million pieces of training data

At Human Science, we are involved in AI model development projects across various industries, starting with natural language processing, including medical support, automotive, IT, manufacturing, and construction. Through direct transactions with many companies, including GAFAM, we have provided over 48 million high-quality training data. We accommodate various types of annotation, data labeling, and data structuring, from small-scale projects to long-term large projects with a team of 150 annotators, regardless of the industry.

Resource management without using crowdsourcing

At Human Science, we do not use crowdsourcing; instead, we advance projects with personnel directly contracted by our company. We form teams that can deliver maximum performance based on a solid understanding of each member's practical experience and their evaluations from previous projects.

Supports not only annotation but also the creation and structuring of generative AI LLM datasets

In addition to labeling and annotation for identification systems for data organization, we also support the structuring of document data for the construction of generative AI and LLM RAG. Since our founding, we have been engaged in manual production as a primary business and service, leveraging our unique know-how gained from a deep understanding of various document structures to provide optimal solutions.

Equipped with a security room in-house

At Human Science, we have a security room that meets ISMS standards within our Shinjuku office. Therefore, we can ensure security even for projects that handle highly confidential data. We consider the protection of confidentiality to be extremely important for all projects. Even for remote projects, our information security management system has received high praise from our clients, as we not only implement hardware measures but also continuously provide security training to our personnel.