Grounding means tying an AI model’s answers to real, checkable sources instead of letting it rely on memory alone. A plain large language model generates text from patterns it learned during training, which means it can sound certain while being wrong. Grounding fixes this by handing the model relevant facts at the moment of the question and asking it to answer from those.
Imagine the difference between a student answering an exam from memory versus one taking an open-book test with the right pages in front of them. The open-book student gives answers anchored to the actual text, with far less guessing. Grounding turns the AI into that open-book student, which is the single most effective way to cut down hallucination.
In practice grounding usually relies on retrieval: the system searches a trusted set of documents, pulls the passages that match the question, and feeds them to the model alongside the prompt. The popular pattern that does this is called RAG.
Done well, grounding also lets the model cite its work. Because the answer was built from specific passages, the system can show which document and which line it came from, so a reader can verify the claim instead of taking it on faith. The catch is that grounding is only as good as the source material: feed it an outdated policy or a wrong figure and it will repeat that error with the same confidence, just now with a citation attached.
At TopDevs we ground AI features in each client’s own documents and data, so the assistant answers from their reality rather than the open internet, and stays accountable to a source.
