How to Build a RAG System: A Most Powerful Tool (01/10)

How to Build a RAG System: A Most Powerful Tool (01/10)

RAG System

Introduction

RAG (Retrieval-Augmented Generation) is a process technology that optimizes the output of large language models (LLMs) by referring to a reliable knowledge base outside of the model’s training data before generating a response. In simple terms, Retrieval-Augmented Generation (RAG) allows LLMs to search through a vast collection of documents for relevant information before generating an answer or text. RAG technology was developed by Facebook AI Research (FAIR) and was first proposed in the 2020 paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Since then, RAG has garnered significant interest in various natural language processing (NLP) research fields.

RAG systems integrate retrieval and generation models to generate more accurate and reliable answers. To build a RAG system, you need a retrieval system that supports real-time information search and a generative model capable of generating answers based on the retrieved information. Additionally, the dataset used should maintain practical accuracy while complying with privacy regulations such as GDPR.

Choosing a Retrieval Model

The key components of a RAG system can be broken down into the retriever, generator, and augmentation methods.

The retriever plays an essential role in the system, responsible for searching for relevant information from large datasets. It bridges the gap between the general knowledge of LLMs and the real-time, contextually accurate information needed. This is especially crucial when the system needs real-time data, expert knowledge in a particular field, or fact-checking.

Integrating a Generative Model

The generator’s job is to take the retrieved results and generate a response for the user. To effectively use the retrieved information, the system performs post-processing steps like re-ranking and information compression. It also optimizes the process to adapt to the input data and generate coherent and relevant answers. Typically, generative models such as GPT, T5, or BART are used to handle text generation.

The generation process involves various optimization methods, including refining the model’s ability to contextualize the retrieved documents and improve the relevance and accuracy of the response.

Augmentation Methods

The augmentation stage plays a crucial role in the training process of language models (LM). This stage can be broken down into three main phases: Pre-training, Fine-tuning, and Inference. Each stage focuses on improving the efficiency and accuracy of the RAG system.

Data Source Augmentation:

The choice of data sources significantly affects the efficiency of a RAG system. Different data sources provide varying levels of granularity and aspects of knowledge, which require different processing methods. These data sources can be broadly categorized into:

• Unstructured data: Includes text documents, articles, and other free-form content.
• Structured data: Includes databases, spreadsheets, and other well-organized data formats.
• LLM-generated content: Content generated by language models themselves, which can be used to augment training datasets.

Optimization of Search Procedure:

Search procedures can be optimized through methods such as iterative retrieval and adaptive retrieval. These approaches involve refining the search process through multiple iterations or adjusting the retrieval mechanism based on specific tasks and scenarios, allowing the system to adapt better to different use cases.

Conclusion: LLM-Based Data Utilization Solutions

Building a custom sLLM (small Large Language Model) for a company can be costly and complex, with scalability issues and the need for ongoing maintenance and expertise. However, using CUBIG’s DataXpert, this process can be simplified. DataXpert is an all-in-one solution that is adaptable to any field, providing RAG-based data utilization and a public LLM integration interface.

Additionally, CUBIG offers solutions like DTS (Data Transform System), which generates high-quality synthetic data, and LLM Capsule, a prompt filtering solution. By combining DataXpert with other tools, companies can create innovative AI solutions that replace inefficient sLLM designs, resulting in a more scalable and effective system.

To learn more about CUBIG, click here. If you’d like to explore more of our blog posts about various solutions and approaches to leveraging AI freely while protecting privacy, click here.