Retrieval-Augmented Generation (RAG)
AI Agents
A technique that retrieves relevant information from a chosen source and gives it to an AI model with the question, so the answer can be grounded in it.
AI Agents
What Is Grounding in AI?
In generative AI, grounding is the practice of connecting an AI model’s answers to specific, checkable sources, such as your documents, a database, or search results, so its responses are based on that material rather than only on what the model learned during training. A grounded answer should let you see where it came from and check it. Grounding is the goal; retrieval-augmented generation (looking up relevant passages before answering) is a common way to achieve it.
Also called AI grounding, grounded generation
Example
A hypothetical school district’s HR team gets the same benefits questions every fall. Does my dental plan cover braces? When does open enrollment close? Can I add my spouse mid-year? The team sets up an AI assistant grounded in the district’s current benefits guide, plan summaries, and enrollment calendar.
When a teacher asks about orthodontic coverage, the assistant answers from the dental plan summary and cites the section it used, so the teacher can confirm it. The assistant is instructed to say when the documents don’t cover a question and to refer the person to an HR specialist. In a spot check, HR finds one answer saying a spouse can be added mid-year, while the cited passage allows that only after a qualifying life event, so they tighten the instructions and keep checking cited passages.
How grounding works
Grounding happens when a question is answered. Instead of answering purely from what it learned in training, the system gathers relevant material, either before the model starts or by letting the model call a search or lookup tool as it works. It then supplies that material to the model, usually with instructions to base the answer on it.
The material can come from several places:
- Your documents, such as policies, manuals, contracts, or a knowledge base.
- Business systems, such as a database, customer relationship management (CRM) system, or HR platform.
- Web search results, the approach behind many AI search tools.
The most common method is retrieval-augmented generation (RAG), which finds the most relevant content and passes it to the model. When the material is short, a system can simply give the model the whole document, as happens when you attach a file to a chat. Because the material is supplied at answer time, you can update the sources without retraining the model, though changed documents often need to be synced before the system can find them.
Despite the similar name, grounding is not the same as ground truth, which refers to reference data treated as the correct answers, often labeled by people, used to train or evaluate a model.
What “grounded in your data” should mean
“Grounded in your data” is a common line in AI sales pitches, but the phrase has no single standard meaning, so ask what it covers in practice. Before you buy, ask the vendor these questions:
- Which sources does it use? You should know exactly which folders, systems, or sites it draws from, and which it ignores.
- Does it show its sources? Answers should cite the document or passage they came from.
- What happens when the sources are silent? Good setups say they don’t know or hand off to a person, rather than filling the gap with general knowledge.
- How current are the sources? Someone needs to own updates and remove old versions, or the tool can repeat outdated information.
- Does it respect permissions? People should only get answers drawn from material they are allowed to see.
Also note that “trained on your data” is a different claim. It usually means the model was adjusted using your material, not that each answer is drawn from a specific source you can check.
If a vendor can’t answer these questions clearly, “grounded” may only mean the model can read some of your files, which is a much weaker claim.
Why citations matter
Citations are what make grounded answers practical to check. A system can base its answers on your sources without ever showing them, and then you have no easy way to confirm what it says. When an answer points to the policy page or report it came from, a reader can open the source and confirm the detail quickly instead of taking the AI’s word for it.
Grounding also tends to reduce hallucinations, confident-sounding statements that are false or not supported by the material the model was given, because the model has relevant facts in front of it. It does not eliminate them.
Treat a citation as a pointer, not proof. Check that the cited passage actually says what the answer claims, especially for anything involving money, legal obligations, health, or employment decisions. A simple habit worth building across your team: for answers that matter, open the source.
What grounding can’t guarantee
A grounded answer is only as good as the material behind it, and even good material can be misused. Common problems include:
- Misreading the source. The model can summarize a passage incorrectly, miss an exception, or mix up details from two documents.
- Overreaching. The answer may go further than the source supports, for example turning “may be eligible” into “you are eligible.”
- Wrong or conflicting sources. If your documents contain errors or contradict each other, the AI can repeat the error or pick the wrong version, often with confidence.
- Missed retrieval. The search step can pull the wrong passages or miss the right one, so the model answers from incomplete information.
- Blended knowledge. Some setups mix what the model learned in training with what the sources say, without making clear which is which.
Grounding is a sound practice, not a guarantee. Clean, current sources and spot checks on important answers matter as much as the technology itself.
Further reading
- Claude 201 WorkshopA hands-on workshop on Claude connectors and Projects, useful if you want Claude to work from your own files and tools.
- AI ImplementationRelevant if you want help connecting your systems and deploying AI as part of a redesigned process.
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