Grounding
Grounding gives an AI model trusted source material and instructs it to base its answer on that evidence. It reduces unsupported claims but does not eliminate model error.
For a docs chatbot, grounding means the model answers from retrieved documentation passages rather than from its training data. The retrieved text is placed in the model's context with instructions to answer only from it.
Grounded systems should link to their source material and decline questions that the available evidence does not support. An assistant that never declines is more likely to answer beyond the retrieved evidence.
Grounding quality depends on retrieval quality. If chunking or indexing misses the relevant section, the model either refuses or answers from a partially relevant passage. Most grounding work is really retrieval work.
Frequently asked questions
How can I tell if a chatbot is grounded?
Ask it something your docs do not cover. A grounded assistant says it does not know; an ungrounded one invents an answer. Source citations on normal answers are the other signal.
Is grounding the same as RAG?
RAG is the most common technique for achieving grounding in docs assistants. Grounding is the property; RAG is one way to get it.