Source grounding is the practice of tying an AI system's outputs to specific, identifiable source documents, so that each claim can be traced back to and verified against the material it came from. A grounded answer carries citations or references rather than relying on the model's parametric memory alone.
Why it matters
Grounding is the primary defense against hallucination and the basis for trust in AI-assisted research: if a claim links to its source, a reader can check it, and an auditor can establish provenance. For consultants and analysts, this is the difference between an answer they can put in front of a client and one they cannot. It shifts AI from a confident guesser to a tool whose output stands on inspectable evidence.
How Pith relates
Source grounding is Pith's core principle. The cited wiki and per-client briefings are constructed only from what you have saved, and every page is grounded in its sources so claims remain traceable. Pith treats the wiki as the artifact and keeps provenance as machinery beneath it, computed and exposed through APIs, MCP, and audit exports rather than as decoration on the page.
See also
Last reviewed: 7 June 2026 · Licensed CC BY 4.0 · cite freely with attribution to Pith.