Open Source Summit Korea 2026

Large Language Models (LLMs) are widely used to search, summarize, and generate answers. They are powerful, but results are not always stable and can be difficult to verify.

Many systems use Retrieval-Augmented Generation (RAG) to connect LLMs with external data. This helps, but does not fully solve the problem. The same question can produce different answers depending on the model or context, making knowledge hard to manage.

This raises a simple question: where should knowledge be managed?

A similar issue exists in software supply chains. SBOM (Software Bill of Materials) makes components and relationships visible, but is often treated as static data.

Based on hands-on experience designing graph-based knowledge systems, this session introduces a design approach: managing knowledge outside LLMs as structured, persistent data.

We discuss lessons from SBOM and how graph-based approaches—using technologies such as SQL/PGQ or GQL—can help understand relationships and trace changes.

The focus is a practical way of thinking for more open and sustainable knowledge practices.