Glossary · 4 · Grounding: content, retrieval and knowledge
Grounding
Also known as: Grounded generation, Source attribution
Grounding is the practice of tying a generative AI model’s output to specific, verifiable sources — retrieved documents, databases, search results or tool outputs — so that answers can be traced and checked instead of relying on the model’s internal knowledge.
- Intermediate
- Technical writers
- Technical marketers
- Technical project managers
- Developers
In one sentence
Grounding in AI explained: tying model answers to verifiable sources — the main defense against hallucinations in documentation and support.
Example
A grounded assistant answers “The maximum operating temperature is 45 °C” and links to the data sheet section it took the value from.
Why it matters on your learning path
- Technical writers: Grounded answers are only as good as their sources. Outdated or contradictory content produces confidently wrong answers.
- Technical marketers: Citations build trust in AI features; they also expose outdated public pages.
- Technical project managers: Require source citations as an acceptance criterion for customer-facing AI.
- Developers: Return source identifiers with every retrieved passage and show them in the answer.