Unlocking the Power of LLMs: How RAG Leads the Way
The rapid advancements in general LLMs, driven by the increase in Data x Parameters, and reinforcement learning with Human Feedback, are notable. However, many enterprises face difficulties in adopting LLMs tailored to their specific industry and unique needs. These challenges primarily arise from three key issues inherent in LLMs:
- Inaccurate answers regarding the latest information: Since they rely only on the information they have learned up to a certain point, they cannot provide accurate answers about the latest events or developments.
- Decreased reliability due to lack of sources: Because they base their answers on previously learned information, they cannot clearly present sources, which diminishes the reliability of their answers.
- A lack of domain-specific knowledge: LLMs can be applied to a wide and diverse range of fields, but they are not able to acquire and utilize the terminology, specialized knowledge, and internal materials used in specific industries.
Consequently, many enterprises are adopting Domain-Specific models to solve the problems of general LLMs and boost productivity and enhance customer satisfaction and revenue.
As a solution for adopting Domain-Specific models, enterprises primarily consider 1) Fine-tuning and 2) RAG solutions. Fine-tuning refers to updating a pre-trained model by incorporating domain-specific data to tailor it into a customized model. RAG(Retrieval Augmented Generation) is a solution that, before generating a response, refers to a reliable knowledge base to provide accurate answers.
While many enterprises use the solution that best fits their needs, RAG solutions unlock the full potential of LLMs. This is due to their 1) cost-effectiveness and 2) ability to handle the fluidity of data. Compared to fine-tuning, which requires additional training of the model, RAG offers a much simpler and more cost-effective way to update with the latest information. By merely updating the vector database, RAG allows for seamless incorporation of new data. So, RAG is particularly advantageous for tasks where the model needs to integrate the latest or domain-specific knowledge from large datasets, such as recent news articles or medical research papers. It is actively utilized in industries where providing accurate information is crucial, such as finance, law, and public institutions.
Then, let’s take a look at how RAG works!
Before setting up the RAG solutions, it is necessary to transform unstructured data into a format understandable by machines. To deliver accurate information through LLMs, unstructured data such as PDF, PNG, etc., needs to be organized to set up the RAG solutions.
Once this process is completed, the RAG solutions can be utilized.
- When a user asks a question to the LLM, the LLM encodes the query and sends it to another model(encoding model).
- The encoding model identifies matching items from a knowledge base index that machines can read. Once matching items are found, relevant data is retrieved, converted into a machine-readable format, and sent back to the LLM.
- The LLM combines the retrieved words with its own response to the query and provides the final answer by citing the sources found by the encoding model.
According to this operational principle, the data in the Knowledge Base can be one of the main factors determining the performance of the model. Enterprises build and continuously update Knowledge Base based on their own data to encourage LLMs to provide contextually relevant responses. It contains data that is more contextually relevant than generic LLM data and can identify the sources of information, allowing for the correction or removal of misinformation.
Crowdworks AI provides the ‘Knowledge Compiler’ to build an LLM-ready database. With unrivaled expertise in AI training data, it ensures the accuracy of the model. Leveraging these capabilities, Crowdworks AI supports the customization of knowledge bases for various industries such as finance and insurance.
Use Cases of Our RAG Solutions
Chatbot Based on Insurance Enterprise C’s Policy Documentation
A Q&A RAG-Based Chatbot to enable internal employees to easily and quickly find necessary information from 200+ insurance policy documents.
Key Tasks of Crowdworks AI:
- Analysis of internal employee workflow processes and current situations
- Designing Q&A requirements
- Analyzing and digitizing insurance documents
- Designing RAG solutions including Chunking criteria
- Service development
Chatbot for Improving Operational Efficiency within Company P Hotels
A Q&A chatbot using the RAG method to leverage internal documents such as business manuals, delegation of authority regulations, and personnel guidelines
Key Tasks of Crowdworks AI:
- Defining business goals and required features
- Analysis of internal documents and designing metadata
- Configuring the chatbot, including designing scenarios
- Ensuring security and final checks
Why Crowdworks AI
We offer an end-to-end solution for enterprises implementing LLMs. While many enterprises want to adopt LLMs, they often struggle to define clear business objectives. Leveraging our extensive experience in the early LLM market, our experts directly engage with clients to understand their needs and define projects. Also, we pride ourselves on preparing critical databases. Crowdworks AI features a Knowledge Compiler that converts documents into machine-readable formats for RAG and provides high-quality data with 99.9% accuracy through verified experts.
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