Grounding
Grounding is the practice of constraining an AI model's answers to a trusted source, such as your documentation, so every claim can be traced back to real content.
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 have two observable behaviors: they cite the source pages their answers come from, and they decline to answer when the docs do not cover a question. An assistant that always produces an answer is not grounded, it is guessing.
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.