In short: Grounding anchors a generated answer in retrieved material. RAG, retrieval-augmented generation, finds relevant content before generation and provides it to the model as context.
How I use grounding and RAG in practice
I define which decision the term should improve, what data or observations it uses and who will change something as a result. I separate source retrieval from generation and check whether the output actually matches what the cited material says. I record the baseline, measurement date and interpretation limits so real movement can be distinguished from changes in tool, sample or query wording.
What to watch
Even a high-quality source cannot prevent an error if the system selects it poorly, removes it from context or combines it with conflicting material. I compare results over time, on a stable sample and with business context. If the term leads to no concrete next step, it is only a label.
Questions for decisions
- Which corpus does the system select sources from?
- Can we trace a claim back to its source?
- How are conflicts and outdated information handled?
- Where is human review necessary?