Grounding

Factual Definition

Grounding, in artificial intelligence, is the process of connecting a model's generated output to verifiable external information sources, ensuring that its responses are anchored in factual, retrievable evidence rather than relying solely on learned patterns.

Paul Truscott's Perspective on Grounding

Paul Truscott's perspective on grounding is that it is the direct remedy to hallucination, and the only remedy that scales, since there is no single source to issue a correction to when a false claim originates from the model's generation rather than a real published statement. His view is that grounding is not a passive property a brand either has or does not have; it is something actively built through deliberate, sustained corroboration depth, giving retrieval-based AI systems enough verifiable, consistent source material that a fabricated claim has less room to take hold.

He treats grounding work as preventative rather than purely reactive, building corroboration depth for a client entity before a hallucination event occurs, rather than only responding once a fabricated claim has already been detected.

How Paul Truscott Applies Grounding

Paul builds sustained corroboration across multiple independent, verifiable sources for every client entity as an ongoing practice, treating grounding as continuous entity maintenance rather than emergency response. Where a hallucination is detected, the existing grounding work is reinforced specifically around the corrected fact to accelerate recovery. Read the full methodology.

Why Grounding Matters to Paul Truscott's Practice

Grounding is the underlying defence against hallucination and the practical output of most of Paul's corroboration-building work, connecting directly to how he assesses Visibility Drawdown risk for every client entity. Read the full analytical foundation this discipline is built on.