Hallucination and grounding

A model hallucinates when it produces text that is fluent, confident, and simply not true. Grounding is the practice of feeding it real facts inside the request so its answer is anchored to something you can check. Grounding does not cure the problem, but it turns a guess into a claim you can trace back to a source.

Why hallucination happens at all

To understand why a model makes things up, you have to be clear about what it is doing when it writes. A language model is a next-word predictor. Given the text so far, it produces the most likely continuation, then the next, then the next, one piece at a time. It was trained to make text that reads as plausible. That is the entire objective it was optimized for: sound like the text it learned from.

Notice what is missing from that description. Nowhere in the process is there a step that checks whether the continuation is true. The model has no built-in connection to the world, no database of verified facts it consults, no internal sense of I know this versus I am guessing. It has patterns. When the patterns it learned line up with reality, the output is true, and most of the time they do line up, which is why these models are useful. But truth is a side effect of the training data being mostly true, not a property the model enforces. When the patterns point somewhere plausible but wrong, the model follows them just as smoothly. That smooth, confident wrongness is a hallucination.

A helpful way to picture it: imagine a student who has read an enormous library but is now sitting an exam with no books allowed and a rule that they must never leave a question blank. For most questions they genuinely remember the answer. For the rest, rather than admit they do not know, they write the most convincing thing that fits, in the same confident hand. A model is that student on every question, because writing the plausible continuation is the only thing it knows how to do. It cannot leave the page blank, and it cannot tell you which answers were recalled and which were invented.

Why the mistakes are confident and plausible

The most dangerous quality of a hallucination is that it does not look like a mistake. When software fails in the usual way it tends to announce itself: an error message, a crash, a blank result. A hallucination arrives in the same calm, well-structured, authoritative prose as a correct answer. There is no flashing warning, because from the model’s point of view nothing went wrong. It did exactly what it always does, which is produce a fluent continuation. The fluency is identical whether the content is true or false.

This is worse than an honest error because it disarms the reader’s guard. We are used to judging confidence as a signal of reliability in people, and the model borrows that signal without earning it. It can invent a statistic, a quotation, a citation to a paper that does not exist, a historical detail, and present all of them with the exact tone it uses for solid facts. The reader has no textual tell to go on. The only defense is to check against something outside the model, which is precisely what grounding sets up.

A hallucination does not arrive as an error message. It arrives in the same confident, fluent prose as the truth, which is exactly what makes it dangerous.

Why code makes it worse

Hallucination is a problem in every domain, but code has features that sharpen it. Software is full of names that are meaningful, regular, and predictable. Functions get names like getUserById or parseConfig. Libraries expose methods that follow strong conventions. A model that has seen millions of codebases has learned these conventions deeply, which is usually a strength. It is also exactly what lets it invent a function that sounds perfectly real.

Suppose your task needs a way to validate an email address, and you ask the model. It may confidently call a method named validateEmail on your library of choice. That is precisely the name such a method would have if it existed. It fits every convention. It reads as obviously correct. And it may not exist at all, because this particular library never shipped that method. The model did not look it up. It produced the name the patterns predicted, and the patterns predict the name a real method would use, whether or not one is there.

The same failure runs through more than method names. A model can invent a configuration option that was never defined, import a package that is not installed, assume a database column that does not exist, or call your own internal function with the wrong shape of arguments because it guessed at a signature it never actually saw. Each of these is plausible precisely because it follows the conventions the model learned. In prose, a plausible falsehood might slide by unnoticed. In code, it eventually meets a compiler or a running system, and then it fails, but the failure can be far from obvious and far from where the guess was made.

There is a further twist specific to code. A hallucinated function name might even run without error if, by coincidence, a function of that name exists but does something different from what the model assumed. Then there is no crash at all, just wrong behavior that looks like working code, which is the hardest kind of bug to find.

Grounding: give it real facts to stand on

If the root cause is that the model writes from learned patterns with no tie to your specific reality, the remedy is to supply that reality inside the request. This is grounding: placing real, relevant facts into the context the model reads before it answers, so its response is built on those facts rather than on a guess. The model still writes fluent continuations, but now the most plausible continuation is constrained by the true material sitting right in front of it.

Take the email example again. Instead of asking the model to recall which method exists, ground the request: include the actual list of methods the library exposes, or the actual source of the relevant file. Now the model is not reaching into a fog of conventions. It can see that the real method is named isValidEmail, or that validation lives on a different object entirely, and it writes the call that matches what is actually there. The general ability to write code is the model’s. The specific fact about your library came from the context you supplied.

The shift is from asking the model to remember to asking the model to read. Remembering is where hallucination lives, because memory in a model is lossy pattern, not a lookup. Reading from supplied facts is far more reliable, because the fact is present rather than reconstructed. Most practical work with these models is, at heart, an effort to move as much of the task as possible out of remember and into read.

Grounding turns the question from what do you remember into what does this say. Remembering is where hallucination lives. Reading from supplied facts is far safer.

A worked example, grounded and ungrounded

Put the two modes next to each other on one real question: which files would break if I rename this function? Asked ungrounded, the model has only its memory of codebases in general. It has never seen yours. So it does what it always does and produces a plausible answer: it names a few files with believable-sounding paths, in a confident tone, and some of them may not even exist. The answer reads like knowledge. It is a guess wearing the costume of knowledge, and nothing in the text tells you which parts, if any, are real.

Now ground the same question. Before the model answers, you supply the actual list of places that call the function, gathered from the real code: this exact set of files, at these locations, each with a real call. The model no longer has to reach into the fog. The plausible continuation is now the one that repeats and organizes the true facts in front of it. Its answer names the files that actually call the function, because those are the ones present in its context. Same model, same question, completely different reliability, and the only thing that changed was what the model could see.

Notice where the true facts had to come from. They are relationships across the codebase, which function calls which, and those relationships are not written down in any single file. Someone or something had to work them out and hand them over. That is the quiet, essential step behind every grounded answer: assembling the real facts to ground with. The model cannot ground itself. It can only use the ground it is given.

Citations and verification

Grounding brings a second benefit beyond better answers: it makes answers checkable. When the facts came from a source you provided, the model can point back to that source, and you can follow the pointer. An answer that says, according to this file, this function is called from these three places, and names the file, is one you can open and confirm. An answer that simply asserts the same thing from the model’s own memory gives you nothing to verify against except the model’s confidence, which we have already seen is worthless as a signal.

This is why grounded systems try to keep a thread back to the source. A citation is not decoration. It is the difference between a claim you can audit and a claim you have to trust blind. For code in particular, a grounded answer that tells you which file and which line a fact came from lets you do the one thing that actually defeats hallucination, which is to look. The habit worth building is simple: for anything that matters, treat the model’s output as a draft to be checked against the ground truth, and make sure the ground truth is close at hand.

Grounding reduces, it does not eliminate

Honesty requires a clear limit. Grounding sharply reduces hallucination. It does not abolish it. The model can still go wrong even with good facts in front of it. It might misread a supplied fact, blend a true detail with an invented one, or reach for its own memory in the gap where you did not supply enough. If the grounding is incomplete, the model will happily fill the missing part with a plausible guess, because filling gaps plausibly is still the only thing it does. Grounding shrinks the gaps. It cannot guarantee there are none.

There is also the quality of the grounding itself to worry about. Feed the model wrong or outdated facts and it will faithfully build a confident, wrong answer on top of them, now with a citation that makes the wrongness look even more trustworthy. Grounding moves the burden of being right partly onto the material you supply. That is a good trade, because you can inspect and control that material in a way you cannot inspect the model’s memory, but it is a burden you now own. The correct mental model is not the model no longer makes things up. It is the model’s answer is now anchored to specific facts I chose and can check, so its mistakes are fewer, more visible, and traceable.

All of this points to the same practical center of gravity. If grounding is the defense, then the value of a system depends on how good it is at finding the right facts and getting them in front of the model, correct, current, and complete enough to leave few gaps. Why a fluent predictor behaves this way traces back to what a large language model is and to the training and inference split that leaves it frozen and unaware of your specifics. How the right facts get retrieved and supplied is the subject of retrieval-augmented generation, and the broader job of assembling exactly the right facts for each request is the work of the context layer.