What is Grounding? — one analogy

1The analogy

A fishing boat drops anchor first thing after reaching a good spot. The current looks calm, but it keeps pushing the boat regardless, and without a line caught on the bottom, ten minutes later the line's dangling somewhere else entirely. From the deck, that's hard to notice — the water looks about the same everywhere.

AI writing has the same current running under it. The pull to keep a sentence flowing smoothly is strong enough that even a sentence that started by reading the source can drift, a few lines later, into something the source never said. So a line gets dropped for every single sentence: this sentence is tied to this exact spot in that source. That act of tying what gets said to real evidence — that's grounding.

When there's nothing on the bottom to catch, the anchor just drags. Rather than forcing the boat to sit still anyway, a fisherman moves to another spot or calls it off for the day — the same way, not saying something when there's no evidence is part of the job too.

2In detail

Sounding plausible is stronger than being grounded

An AI writes by looking at everything built so far and picking whatever comes next. That process is extremely good at stitching sentences together smoothly. The trouble is that smoothness and factual accuracy are two entirely separate things.

So even with source material sitting right there, an answer leaking past it is common. Quote three lines from the source exactly, then in the fourth line slip in "so in cases like this, generally" and tack on a guess of its own. The transition reads so naturally that a reader can't spot where the line was crossed.

The way a current pushes a boat, the pull to keep writing pushes a sentence past the edge of the source. That's exactly why a line needs to be dropped down and caught.

A line gets dropped for every sentence

The most common way to tie things down is attaching a source next to each sentence. That's different from tacking a reference list onto the whole answer. Checking requires pointing to exactly where a specific sentence came from.

Narrowing the scope with an instruction helps too — something like "only use what's in the given material, and don't write anything that isn't there," drawing a clear boundary. Some setups even split the instruction between parts that must be quoted verbatim and parts that can be summarized.

Fixing the answer's format helps as well. Have it write one line of claim followed by one line of the original text that backs it up, and an untethered sentence stands out immediately.

Say nothing when there's nothing to tie down to

This is the hardest part. For a person or an AI, "I don't know" is a hard answer to give — a plausible-sounding sentence feels more useful than saying nothing.

So what to do when there's no evidence has to be decided ahead of time: say so when it's not in the material, separate the certain parts from the uncertain ones, or ask a follow-up when confirmation is needed. Leave that undecided and a model fills the blank with whatever it already happens to know.

In real work, this rule often matters more than the quality of the answer itself. One wrong answer can wipe out the value of ten right ones.

Checking whether the line actually caught

Assuming a source is fine just because it's attached is risky. A number gets attached and the original says something else entirely — more often than it sounds. Feed in a passage on a similar topic and a model will use it as evidence anyway.

That's why checking is necessary. The basics: pick out numbers, dates, names, conditions — anything that's a problem if wrong — and match them against the original. At scale, an automated pass compares each answer sentence to the source and only hands the mismatches to a person.

This is the equivalent of tugging the anchor line by hand after dropping it. Skip the tug and there's no way to notice an anchor that's only pretending to have caught.

A different step from fetching the material

Fetching material and setting it nearby has its own name: RAG (Retrieval-Augmented Generation). That's the part where the boat gets steered to a good spot.

Grounding comes after that. Even a well-chosen spot lets the boat drift if the line never gets dropped — which is exactly why a good source can be sitting right there and the answer still leaks past it. Fetching material well and making sure the answer stays inside what was fetched are two separate things that both need attention.

It also works the other way — grounding can be talked about with no material fetched at all. Tying an answer to a value from a calculator or a database lookup is the same act. Whatever the evidence is, what matters is whether a line connects what got said to it.

3More precisely

Grounding refers to the whole body of work that connects a model's output to outside evidence and keeps it inside that boundary. It spans everything from pasting reference material into a prompt, to having a source attached per sentence, to comparing the answer against the evidence and filtering out sentences that drift. More broadly, it can also mean tying language to something outside language altogether — an image, a sensor reading, a database.

The analogy breaks down somewhere. Tug on an anchor line and a hand can feel whether it caught; a source a model attaches doesn't come with any such tell just from looking at it on the page. A number can get attached to a passage that doesn't actually back it up, and several passages can get blended together into a sentence that matches none of them individually. So a source citation is a device that makes checking easier for a person — it doesn't do the checking on its own, and treating it as proof skips the step that actually matters. A separate check comparing the answer against the evidence has to run alongside it to actually earn its keep.

4Try it yourself

Perplexity (AI search) ailearn.space Click the source number attached to each sentence of an answer and check whether the original really says that

5Common misconceptions

It's easy to think a source being attached means the evidence has been confirmed, but actually a number sometimes gets attached even when the original doesn't say that.
It's easy to think handing over material is enough to count as grounding, but actually a guessed-in sentence can still slip through even with the material sitting right there.
It's easy to think grounding is just another word for search, but actually search fetches the material, and grounding is the separate act of tying the words to it.

7One-line summary

In shortGrounding is like dropping anchor so a current doesn't carry a boat off — tying every sentence of what an AI says to real evidence, and staying silent when there's nothing to tie it to.

Did this analogy help?

Spotted an error or have a better analogy? Suggest an edit · Last updated2026-09-02