How to use RAG chatbots created by LLM experts
Getting started
RAG (Retrieval-Augmented Generation) is a technology that uses external knowledge sources to supplement the limitations of LLM (Large Language Model) and generate more reliable answers. Twig Farm's LETR WORKS (LETR WORKS) provides various functions for content management, and by combining it with the RAG system, it is possible to implement even more advanced AI technology. Let's take a look at RAG and LLM and see how they are used in Twigfarm's LETR WORKS.
RAG (Retrieval-Augmented Generation)?
RAG is a technology that integrates large-scale language models (LLM) and external knowledge sources to generate more accurate and reliable responses. This process is achieved by LLM searching for reliable information outside of existing learning data and incorporating it into answers.
RAG's Core Processes
- Generating external data: Collect the latest data from documents, databases, APIs, etc. within the organization and store it in a vector database.
- Search for related information: Vectorize user questions, retrieve relevant data, and return it.
- Prompt reinforcement: Strengthen LLM's answer generation process by augmenting user questions based on retrieved data.
- Updating information: Maintain the freshness and reliability of external data through continuous updates.
Advantages of RAG
- cost efficiency: New information can be incorporated without relearning the LLM.
- Providing the latest information: Provides real-time updated responses using external data sources.
- Increase user trust: Secure credibility by specifying the source in the answer.
- Increased developer control: Maintain model reliability by managing data sources and search results.
Use cases
- Smart chatbots within the enterprise: Providing up-to-date information-based responses from various departments such as human resources and customer service.
- Knowledge search system: Fast and accurate information retrieval based on complex technical documents or research reports.
LLM (Large Language Model)?
LLM is an AI model that performs natural language processing tasks (such as answering questions, translating, and generating text) by learning from vast amounts of data. Examples include GPT-4, Palm, and GPT-Neox.
Limitations of LLM
- Based on static data: Information after the time of study cannot be reflected.
- hallucination (hallucination): Generating information that doesn't exist.
- Terminology confusion: Misinterpretation of various contexts of the same term.
- Lack of reliability: Difficulty providing a source or context for responses.
LLM and RAG synergy
RAG can improve LLM's response quality. RAG compensates for the shortcomings of LLM, and is particularly useful for ensuring freshness and context.
Using LETR WORKS to enhance RAG technology
Data consolidation by domain
LETR WORKS is a platform that can systematically manage and process organization-specific data (translated documents, subtitle data, etc.). Using this to provide domain-specific data to the RAG system can have the following benefits:
- Improved accuracy: Provides domain-optimized responses using professionally translated multilingual data and subtitle materials as RAG's external knowledge source.
- Various industrial applications: Generate highly accurate answers to questions in specific domains such as broadcast, film, and healthcare.
Integration with LETR AI
LETR WORKS LETR AI provides AI technology optimized for data quality improvement and analysis. If you combine this with RAG:
- Enhancing data quality: The RAG model generates accurate and reliable responses by providing high-quality datasets.
- Ongoing data updates: Keep RAG's external knowledge sources up to date by quickly reflecting changed content.
Multi-language support
LETR WORKS translation and dubbing capabilities can greatly contribute to the global scalability of RAG technology.
- Multi-language response: Use a translated database to generate natural and accurate answers in multiple languages.
- voice interface: Using CloneVoice AI dubbing technology, voice-based user experiences can also be supported.
Using SyncSub and ExSub
LETR WORKS automatic subtitle adjustment (SyncSub) and subtitle OCR (ExSub) technology provides the following innovations to RAG systems:
- Text-based data enrichment: Generate responses based on audiovisual materials using subtitle data as an external knowledge source.
- Efficient data processing: Easily manage large-scale data by automating subtitle generation and adjustment.
The synergy between RAG and LETR WORKS
Generate reliable responses
- LETR WORKS excels at refining data and complementing context, so RAG systems can provide reliable source-based responses.
- To the generated response Cite sourcesBy doing so, user trust can be further strengthened.
Stay up to date
LETR WORKS manages content that is constantly updated. This allows RAG to access the latest information in real time.
- Examples: News data, the latest translated content, and subtitle materials can be provided to the RAG system to respond to the latest questions.
Implementing a cost-effective system
- By utilizing LETR WORKS' automated data processing function, RAG system construction and operation costs can be reduced.
- Updating only external data without additional training is efficient because model performance can be maintained.
Linkage with ESG subtitles
By utilizing LETR WORKS ESG (Environmental, Social, Governance) customization features, the RAG system can also realize social value.
- Example: Implementing an accessible chatbot based on subtitle data for the deaf.
Potential development of RAG technology through LETR WORKS
- Intelligent domain chatbot
- Development of RAG-based chatbots tailored to specific industries using LETR WORKS data processing capabilities.
- Example: In the medical domain, building a professional consultation chatbot using translated medical documents.
- Global service expansion
- It is possible to build a multilingual support system for users around the world by combining it with multilingual translation data.
- Content-based search system
- ExSub data is used to search for specific keywords or phrases in video content, and the content is returned as a text response.
- Tailor-made education platform
- Building an education platform that integrates RAG technology with LETR WORKS data to provide customized learning materials for each user.
corollary
Twigfarm's RAG chatbot is a powerful tool that can achieve innovation in various fields such as chatbots, translation, and subtitle generation through a combination of years of LLM research and innovative technology. In particular, based on data quality control, multi-language support, and automated data processing technology, RAG's response accuracy and freshness can be raised to the next level. This will be the foundation for Twigfarm to gain a more competitive edge in the global AI market.
Editor/Choi Min-woo
