What is grounding in AI?
Grounding is the practice of tying every generated answer to specific source text, so the answer reflects that text rather than the model's general knowledge.
A grounded system retrieves passages before it writes anything, puts them in front of the model, and instructs it to answer only from what it was given. The test of whether a system is grounded is simple and does not require access to its internals: ask it something, then ask it where the answer came from. A grounded system names a page. An ungrounded one describes its reasoning.
Why it matters
Grounding is what makes an AI answer auditable. When a customer disputes something the representative said, a grounded system lets you open the exact page and either confirm the answer or fix the page. Without it, the only response available is a shrug — nobody can say why the model said what it said, and the same question may produce a different answer tomorrow.
Examples
- A visitor asks about international delivery and the answer quotes the shipping page, with a link to it.
- A question about a plan limit is answered from the pricing page rather than from what similar products typically charge.
- A question the website has never addressed produces a refusal and an offer to pass it to a person.
Not the same as
Fine-tuning
Fine-tuning changes the model's weights and cannot be audited per answer. Grounding leaves the model alone and changes what it is shown at question time.
Related
term
What is RAG (retrieval-augmented generation)?
RAG is what separates a chatbot that knows your business from one that guesses. Documents are split into passages and indexed by meaning; each question retrieves the closest passages, and the model is instructed to answer only from them.
term
What is an AI hallucination?
Language models predict plausible text, not true text. Asked a question with no supporting material, a model does not fall silent; it produces the most likely-sounding answer, which is often a wrong price, a policy that does not exist, or a citation to a page that was never written. The behaviour is not a bug to be patched out of the model. It is what the model does when nothing constrains it.
term
What is a citation in an AI answer?
A cited answer tells the reader not just what the answer is but where it came from. In a customer-facing system, that link is usually a page on the business's own website, so following it is both a verification step and a way further into the site.
term
What is chunking in AI retrieval?
A whole page is the wrong unit to retrieve. A returns policy page might cover eligibility, timing, refunds and exchanges; retrieving all of it to answer one question about timing buries the relevant sentence in three paragraphs of noise. Chunking splits it so that each part can be found on its own. The split usually follows paragraph or heading boundaries, because those are where a human author already decided one idea ended and another began.
use case
Answer customer questions from your own content
Answering questions well means answering them from something true. RubyRep retrieves the relevant passages from your site before it writes anything, and cites the page it used, so an answer can always be checked.
industry
AI customer support for law firms
People contacting a solicitor are usually anxious and are asking process questions, not legal ones: what this costs, how long it takes, whether the first conversation is confidential. Those answers are on your website and RubyRep can give them instantly — while refusing, by design, to do anything that looks like advice.
RubyRep is this, applied to your website.
Give it your URL and it reads your pages, then answers your customers from them.
Train My RubyRep