Overview
How might we design knowledge graph so people can trust and act on AI output?
When this pattern fits
- Ideal for research tools, RAG systems, and knowledge management applications where visualizing information relationships builds trust and understanding.
When to skip or lighten it
- Simple FAQ bots where a short citation list is enough.
- Mobile-first chats with no room for a graph canvas.
- Answers grounded in a single document with no meaningful relations.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Decorative graphs that do not link back to readable sources.
Hairball layouts with dozens of unlabeled nodes.
Graphs that imply certainty for weak or inferred edges.
No filter or search, forcing users to pan endlessly.
How products use it
| Product | Implementation |
|---|---|
| Hebbia | Document and concept maps for research workflows. |
| Obsidian | Local graph views of linked notes and references. |
| Roam Research | Bidirectional link graphs for knowledge bases. |
| Logseq | Outline-linked graph navigation for personal knowledge. |
Implementation
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Generate a production-ready implementation of the "Knowledge Graph" AI interface design pattern.
Pattern Definition:Frequently asked questions
When does a knowledge graph help AI UX?
When users must understand relationships across many sources—research, investigations, or enterprise knowledge—not for one-shot chat answers.
How does it relate to retrieval preview?
Retrieval preview lists chunks. A knowledge graph shows how entities in those chunks connect. They often sit side by side.
What makes a graph trustworthy?
Clickable nodes that open sources, labeled edge types, and honest treatment of inferred vs extracted links.
Should every RAG product ship a graph?
No. Start with citations and source browser. Add a graph when relation questions dominate user tasks.