Glossary
What is Source-Grounded AI?
Source-grounded AI describes AI systems that produce answers traceable back to specific source documents, decisions, or attributions. Source-grounded answers carry sourcing, status, and confidence so humans can verify what the AI is reasoning over. The opposite of source-grounded AI is unattributed LLM output: answers that sound plausible but cannot be audited.
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Capabilities
What Source-Grounded AI does
Per-claim attribution
Every claim an AI surface produces points back to the source it came from: a document, a decision, a customer call.
Status and confidence on each source
Sources are marked as validated or emergent, with confidence levels and last-updated dates that the AI can surface in its answer.
Reduces hallucination
When an AI must ground its answer in retrievable sources, it has less room to invent plausible-sounding but wrong content.
Supports auditing
When a human reviews an AI answer, they can trace back to the source and verify or correct it.
Distinctions
Source-Grounded AI vs adjacent concepts
Source-Grounded AI is often confused with related but distinct ideas. Here is how it differs.
| Concept | What it is | How Source-Grounded AI differs |
|---|---|---|
| RAG | A retrieval mechanism that fetches context per query. | A property of the answer: that it carries source, status, and confidence. RAG is one way to deliver source-grounded AI; a typed knowledge graph is another. |
| Unattributed LLM output | Plausible-sounding answers with no traceable sources. | Every claim links to source. Auditable, verifiable, correctable. |
| Citations in chat AI | Manual or hyperlink citations attached to an answer. | Per-claim sourcing built into the structured knowledge layer, not bolted on at the chat UI. |
Who uses it
Who uses Source-Grounded AI
Founder-operators, regulated industries, and teams whose decisions depend on AI output that has to be auditable. Any company using AI for board reporting, investor updates, customer-facing content, or product decisions where 'plausible but wrong' is unacceptable.
FAQ
Common questions about Source-Grounded AI
How does DearTech-OS make answers source-grounded?
Every node in the DearTech-OS knowledge graph carries source, status (emergent or validated), confidence, and ownership metadata. When an AI tool queries the graph, retrieved nodes come with their sourcing attached. The AI can surface those sources in its answer, so the team can verify what the AI is reasoning over.
Why does sourcing matter for AI tools?
Without sourcing, AI output is plausible by default and unverifiable. Teams cannot tell what is true, what is current, or what is invented. With sourcing, AI becomes a tool that augments human judgment instead of replacing it with confident-sounding guesses.
Is source-grounded AI the same as RAG?
No. RAG is a retrieval mechanism. Source-grounded AI is a property of the resulting answer: that every claim carries traceable sourcing. RAG is one way to deliver source-grounded AI, and so is graph-based retrieval, and so is structured tool use through MCP.
Related terms
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See Source-Grounded AI in practice
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