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| Company name | 로이드케이 |
|---|---|
| CEO | |
| Category | Things - AI Module, AI Solution Environment - Disaster Prevention (AI prediction, AI maintenance, etc.) Convergence - Robot, AI Services (Recommendation System, Search Engine, Interpretation, Consulting, etc.) Human - AI Ethics, Security (Telebio, Bio-Authentication, Gateway, etc.) Data - Big data |
| brochure | |
| Website | https://lloydk.co.kr/ |
| Company introduction | Certainly, here is the English translation of the provided Korean text: Large Language Model (LLM)-based external generative AI services have increased accessibility and user satisfaction by generating favorable responses based on their trained information when services are requested in natural language. However, they fall short of being able to fully replace in-house corporate tasks. For a company to utilize an external LLM for its business operations, it must consider factors such as internal data linkage, providing expert knowledge, LLM training costs, and protecting internal corporate information. As a solution to these challenges, enterprise-grade Small Language Models (sLLMs) leveraging RAG (Retrieval-Augmented Generation) and Vector Databases (Vector DB) are gaining prominence. The first steps for building a Vector DB to search and reference knowledge sources in a RAG system are: Data Collection/Extraction Data Cleansing Document Splitting (Chunking) Embedding Preparation Metadata Generation Chunking is the process of dividing long documents or text data into small, meaningful chunks. These chunks are made into a format that is easy for the AI to search and understa |
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