Grounding

AI grounding ties the important claims in an answer to evidence you can inspect. If a client asks whether extra work is included, you need the answer to come from their signed scope and approved changes, with the relevant clause there for you to check.

Finding a document isn’t enough, because the model can quote the wrong version or add something the document never said. Grounding gives you a way to follow the claim back to its support, and the firm still needs to decide which record is authoritative and who may see it.

What is the difference between grounding and RAG?

Retrieval-augmented generation, or RAG, brings outside information into the model’s request. Grounding asks whether the answer stays supported by that information.

Suppose retrieval finds the right contract clause, but the model adds a 30-day notice rule that isn’t there. The system used RAG, but that extra rule is ungrounded.

Google Cloud describes RAG as one common grounding technique, alongside sources and methods such as web search, maps, managed RAG, Elasticsearch, custom search APIs, and other web indexes. Whatever brings the information in, you still need to trace the claim to evidence that supports it.

Fine-tuning does a different job, teaching patterns such as a format or tone rather than fetching today’s signed scope. And context engineering chooses the instructions, records, tools, and history the model receives, while grounding asks whether its answer stays faithful to the evidence in that context.

What does a grounded answer look like in service delivery?

Take the change request from the opening. The system should fetch the signed scope and approved changes for that client, identify the clause that controls the answer, and cite the version it used.

If two approved records conflict, it should say so and send the decision to someone who can resolve it. You don’t want the model choosing whichever version makes a cleaner answer.

An old proposal in the shared drive shows why this matters. Its wording may match the question perfectly and the model may quote it accurately, but the signed agreement still decides the current scope.

The right source depends on the fact. Scope belongs to the agreement and approved changes, account status to the live client record, and a service standard to the approved procedure.

A public search result may help with a law or product release, but it is a poor source for a promise your firm made to one client. The record also needs a date, an owner, and rules about who may see it.

AWS makes that limit explicit in its grounding and RAG guidance, a grounded response is only as reliable as the document, database, or API behind it. Its guidance also calls for source checks, traceability, and access control.

Why is a citation not enough?

A citation tells you where to look, not whether every part of the sentence is supported. You still have to compare the claim with the passage.

Google Cloud’s grounding check uses the example “Google was founded by Larry Page and Sergey Brin in 1975.” The founders are right but the year is wrong, so it treats the whole claim as ungrounded rather than giving it a pass for being partly right.

The API returns an overall support score from 0 to 1 and can return scores and linked text for individual claims. For review, I would want the claim and its evidence beside each other, not just the overall number.

A June 2024 NAACL Findings study found ungrounded sentences across three datasets and four model families, including responses that contained the correct answer. So getting the conclusion right doesn’t tell you whether the reason, date, exception, or recommendation beside it is supported.

Google DeepMind’s FACTS Grounding benchmark uses 1,719 examples with source documents up to 32,000 tokens. It asks for both a useful answer and traceable support, because refusing everything may avoid false claims without doing the job.

What else can fail in a grounded answer?

I would check the whole path the evidence took. Start with whether this is the record that owns the fact, then whether retrieval found the right passage and version, then whether that passage supports the whole claim.

Relevance and permission need their own checks. A true fact about another client doesn’t answer this client’s question, and an accurate answer from a record the user cannot access is still a security failure.

Then look at age and completeness. A January policy may support the exact sentence written in August and still be out of date, or a clause may be quoted correctly while an approved amendment changes the answer.

Finally, check whether the workflow stops when evidence is missing or conflicts. Otherwise the model may fill that gap with something plausible, even after the earlier steps worked.

NIST’s July 2024 Generative AI Profile recommends checking where retrieval data came from, checking support, reviewing generated sources and citations, and reassessing risk after retrieval or fine-tuning changes. Adding a knowledge source changes what the system can answer from, so test the new setup.

How should a business test grounding?

Pick one delivery decision with a clear source of truth, such as a change order, refund, proposal price, or compliance statement. That’s easier to judge than whether a general company chatbot is “good.”

Start with five cases, a normal one, an old source beside a current one, conflicting sources, a missing record, and a record the test user cannot access. For each, write down the expected source, what the answer may claim, and when it should refuse or ask for approval.

Check retrieval first, including client, version, date, and permission, then compare the answer’s important claims with those passages. That separates a failure to find the source from a failure to use it properly.

Save the cases as an eval and rerun them when a source, index, prompt, model, tool, or workflow changes. Keep the question, passages, versions, answer, support result, and final human decision, so a failed score gives you something you can investigate.

The process owner decides which record is authoritative and when it changes. The implementation owner handles access rules, retrieval logs, and tests, while the reviewer owns the approval point for claims whose consequences are hard to reverse.

Where does grounding sit on the Delivery Model Ladder?

Grounding belongs mainly at Stage 2, Augmented, on the Delivery Model Ladder. The workflow retrieves approved evidence, keeps citations, checks support, and sends exceptions to a named person.

At Stage 1, someone may open a contract, paste it into chat, and check the answer by hand. That can help, but the source choice and checking method still depend on that person doing the work each time.

At Stage 3, more of that preparation runs automatically, fetching current records, applying permissions, checking support, and routing weak evidence. People still own the sources and the tests.

This checking work is part of the Production Gap. AI can write an answer in seconds while someone spends the saved time finding newer records and tracing claims, so grounding helps only when the evidence and checking come with the deliverable.

When is grounding not enough?

A bad source stays bad, and a supported answer can still need judgment about whether acting on it is wise, fair, or safe. Missing clauses, conflicting policies, and restricted records don’t stop needing an owner because the system can cite them.

Before moving a workflow forward, check which record owns the fact, which passage was retrieved, whether it supports the whole claim, whether this user may receive it, and what happens when evidence is missing. If those answers aren’t clear, keep a person in the decision.