Grounding
The property of an AI system's answer being tied to a specific, defined source of information (either loaded organizational context for the AI Briefcase, or verified program content via retrieval for the professor) rather than drawn from unconstrained general training data. Grounding improves relevance and consistency; it does not, by itself, guarantee current or verified accuracy.
Defined in 3 GAGE programs, which carry 4 distinct definitions of it. The wording above is taught in AI Data Governance: The Data Chair.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
The property of an AI system's answer being tied to a specific, defined source of information (either loaded organizational context for the AI Briefcase, or verified program content via retrieval for the professor) rather than drawn from unconstrained general training data. Grounding improves relevance and consistency; it does not, by itself, guarantee current or verified accuracy.
Supplying a model with authoritative source material (usually through retrieval) so that its answer is anchored to that material rather than generated from general training alone. Grounding is the main configuration defense against confident invention, and it is why the context dial is central to reducing hallucination. (see Topic 1.5)
Anchoring an AI's output in supplied source material and instructing it to use only that, shifting the model from guessing to summarizing and measurably reducing fabrication.
Whether a genuine source actually supports the specific claim attached to it, and at the strength claimed. The join between claim and source, distinct from provenance.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- Communicating WITH AI: Precision, Structure, and Context-Setting · Practical AI Workflow Design and Prompt Engineering, AI Literacy & Professional Conduct
- Source-to-Output Verification Protocols · Critical Thinking and Context Engineering, AI Literacy & Professional Conduct
- Your command tools: the Briefcase, the professor, the dossier you will build · Taking the Data Chair, AI Data Governance: The Data Chair
- Prompts, context, and why the same model gives different companies different answers · Build Before You Govern, AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.