AI grounding is the practice of tying an AI model's answers to verifiable sources, such as approved documents, account records, or tool results. A grounded answer can be traced back to the source it came from.
Google Cloud's documentation defines grounding as "the ability to connect model output to verifiable sources of information." For a bank or an insurer, those sources are the current policy wording, the customer's own account, and the rules that decide what may be offered.
What an AI agent grounds its answers in
Approved knowledge. Product terms, fee tables, help articles, and procedures, usually held in a knowledge base.
Systems of record. Balances, order status, claim status, and due dates, fetched through an API call during the conversation.
Business rules. Eligibility, limits, and the exact wording of required disclosures, as the company has written them.
The conversation itself. What the customer has already said, such as a reference number or a date.
Without grounding, a model answers from what it learned in training. That knowledge is general, may be out of date, and contains nothing about a particular customer.
Grounding, RAG and hallucination
Grounding describes the result, an answer backed by a source. Retrieval-augmented generation (RAG) is one way to get there: it searches documents and places the relevant passages in the model's input. Live API calls to back-end systems are another.
The paper that introduced RAG, by Patrick Lewis and 11 co-authors (NeurIPS 2020), notes that language models store facts in their parameters. It calls providing provenance for their decisions an open research problem.
Weak grounding shows up as hallucination. NIST's Generative AI Profile (July 2024) calls it confabulation: "confidently stated but erroneous or false content". In customer service, the costly cases are often small, such as a wrong fee or a rate from last year's terms.
How grounding fails
The source is wrong or stale. A grounded answer repeats whatever its source says. An old fee table produces a well-sourced answer that is wrong.
The wrong passage is retrieved. A question about a business account pulls in the terms for a personal one.
The model goes beyond the source. It fills a gap with general knowledge, or blends two passages into a statement neither contains.
Numbers drift in the retelling. Microsoft's groundedness detection documentation gives an example. The source says the interest rate is 4.5%, the answer says 5%, and the tool flags the 5% as ungrounded.
How teams check grounding
Groundedness checks. An automated checker compares each answer with the sources it was given and flags statements they do not support. Microsoft's tool works this way for summaries and question answering.
Source records. Logging which document version and which API response sat behind each answer lets a reviewer reproduce it later from the audit trail.
Test sets. Questions with known answers show whether the agent gets them right. Questions the sources cannot answer show whether it declines or hands over.
Figures from systems. For amounts, rates, and dates, many teams take the figure straight from the system of record or a rules engine. The model only phrases it (see deterministic execution).
Frequently asked questions
What is the difference between grounding and RAG?
Grounding is the aim of backing each answer with a verifiable source. RAG is one technique for it: it retrieves passages from documents and adds them to the model's input. Live API calls, rules engines, and structured databases can ground answers as well.
What does grounding mean in generative AI?
It means connecting what a model generates to sources outside the model, so that each statement can be checked. A grounded answer usually carries a reference to the passage or record it used. Without such sources, the model relies on its training data, which may be general or out of date.
Can a grounded AI agent still hallucinate?
Yes. The model can misread a passage, combine two sources, or add details the sources do not contain. Grounding lowers the risk, and checks on each answer catch some of what remains. Facts that must be exact, such as amounts and dates, are safer taken directly from the source system.
How do you measure groundedness?
Teams usually compare a sample of answers with the sources behind them, using reviewers, an automated checker, or both. The share of statements the sources support is the usual figure, reported per topic and per language. Microsoft notes that its detector is optimized for English, so other languages may need more human review.
How we approach this at Zowie
Zowie's AI agents work on voice, chat, and email, with over 100 million conversations a year across our clients. The language model speaks. A separate, deterministic Decision Engine returns every amount, rate, offer, disclosure, and escalation from your rules, with an audit log. To see how that works on your own policies, you are welcome to book a demo.
Explore: Retrieval-augmented generation (RAG), AI hallucination, Hallucination prevention, Zero hallucination architecture, Knowledge, Deterministic execution