Why do AI models hallucinate — and does grounding them in real sources fix it?

Short answer: No — grounding does not cure hallucination. It reduces it substantially, and it changes the kind of error you get: from inventing a source to misreading a real one. What remains is checkable, which is the part that matters. Retrieval on its own does not remove a model's incentive to guess.

We build a research engine that hands scripture, morphology and lexicon entries to an AI as tool results. So we have an obvious interest in the claim that grounding fixes hallucination. This post is the honest version of that claim, including the study that cuts against it.

What counts as a hallucination?

A hallucination is a plausible falsehood stated with the same confidence as a fact — an invented citation, a verse that does not exist, a Strong's number that was never assigned. It is not a bug in the ordinary sense. Models rarely misspell words or produce nonsense; they produce specific, well-formed, wrong facts. That specificity is the tell, and it points at where the behaviour comes from.

Why do they do it?

The most useful answer published so far is that hallucination is an incentive problem, not a mystery. Kalai, Nachum, Vempala and Zhang at OpenAI set this out in Why Language Models Hallucinate, published in Nature in 2026: the benchmarks the whole field optimises against score accuracy, which gives no credit for saying "I don't know" and full credit for a lucky guess. A model trained and selected under that scoring learns to bluff.

Their illustration on the SimpleQA evaluation is the clearest version of it. One model answered nearly every question and was wrong more than three quarters of the time. Another abstained on about half and made far fewer errors — and still scored worse on accuracy, because abstention earns nothing. If the scoreboard rewards guessing, the models guess.

This has a consequence people miss when they reach for retrieval as the fix. The same authors are explicit that web search and scaling will not push accuracy to 100%, because some questions have no available answer. The incentive to guess survives the addition of a search tool.

How often does it actually happen?

Often enough that the headline numbers sound implausible until you read the definitions. Three benchmarks measure three different things, and quoting one without its definition is how the confusion starts.

The most-quoted figure of 2026 is a fourth measurement again, and it is usually quoted wrong. Stanford HAI's 2026 AI Index reports hallucination rates from 22% to 94% across 26 top models — not on any of the three above, but on a benchmark probing whether a model can tell knowledge from belief. The detail underneath it is the one that should worry you: when a false statement is put to a model as something another person believes, it handles it well. When the identical statement is framed as something you believe, performance collapses. GPT-4o's accuracy fell from 98.2% to 64.4%; DeepSeek R1's from over 90% to 14.4%.

That is a different failure from the other three, and it is the one that bites hardest in Bible study, where a reader almost always arrives with a premise. Ask an assistant to confirm what a Greek word means and you may get an answer shaped by what you signalled you expected.

So does grounding help, or not?

Both, and the distinction is the whole point.

The Stanford RegLab study is usually cited as the debunking of retrieval-augmented generation, and on the marketing claims it is. But read what it actually measured: the retrieval-grounded legal tools hallucinated less than general-purpose GPT-4 on the same queries. Grounding worked. It just did not work anywhere near as well as the people selling it said.

Where retrieval fails is specific and worth knowing:

• Relevant but insufficient. The retrieved passage is on the right topic and does not contain the answer. The model fills the gap.
• Conflicting sources. Two retrieved documents disagree; the model picks one and reports it as settled.
• Lost in the middle. Long contexts bury the decisive sentence where the model attends least.
• Compounding. In agentic loops, a hallucinated intermediate result becomes the input to the next retrieval, and the error propagates.

What grounding actually changes

Here is the honest formulation, and it is stronger than the marketing one: grounding does not make an AI right. It makes it wrong in a way you can catch.

An ungrounded model that invents Thayer's entry for a Greek word hands you a fabrication with no thread back to anything. A grounded one that misreads the real entry hands you a claim, a lemma and a reference — and thirty seconds of checking settles it. The first failure is invisible. The second is auditable. In any domain where being wrong has a cost — law, medicine, a sermon on Sunday — that difference is the entire value.

It follows that the design question is not "how do we stop it hallucinating" but "what do we hand the model, and can the reader check it?" Which is why we made Darash return raw materials with citations attached rather than conclusions. A conclusion cannot be checked. A Strong's number, a parsing and a named lexicon can.

And it is why the corpus underneath has to be right. When we rebuilt Thayer's Greek Lexicon from the 1889 Harper & Brothers print, we transcribed all 716 body pages from the page images and collated the witnesses against each other: they agreed on 98.91% of 856,793 tokens, and every contested spot was adjudicated against the page image rather than settled on whichever reading was more fluent. The rule was that no character may exist that is not on the page — one illegible Josephus digit is still served as a question mark, because filling it in would have been a hallucination with our name on it.

We publish that 0.19% measured error bar instead of claiming the corpus is perfect, which is the same discipline this whole post is about: a stated error rate is checkable, and a claim of 100% is not. The full method is published, defects and all.

What this means if you study with an AI

Three things follow, none of which require you to buy anything:

1. Ask for the reference, always. A model that cannot name where a claim came from is reciting, not reading. Make the citation the deliverable, not a courtesy. Our ordering of which Bible study tools to reach for first is built on the same rule.
2. Treat confidence as noise. Fluency and certainty carry no information about correctness. The benchmarks above measure exactly this and find no reliable relationship between a model's general capability and its factual reliability.
3. Give it the sources, then check them. Grounding shifts the work from trusting to verifying. Verifying is work — much less work than being quietly wrong for a year.

Frequently asked

Does RAG eliminate hallucination?

No. The Stanford RegLab study found retrieval-grounded commercial legal tools still hallucinating in more than 17% of queries, despite vendors describing the problem as solved. Retrieval reduces hallucination; it does not remove it.

Why can't the labs just fix it?

Because the incentive sits in the evaluation, not only in the model. As long as the benchmarks that define progress award nothing for admitting uncertainty, models are selected for confident guessing. The OpenAI paper's proposal is to change the scoring, not just the models.

Do newer models hallucinate less?

Somewhat, and unevenly. General capability does not predict factual reliability — Artificial Analysis found models with strong overall intelligence scores and poor hallucination rates, and the reverse. Pick for the property you need.

Is a low hallucination rate always good?

No, and this is the trap in the numbers. A model that refuses most questions posts an excellent rate by never risking an answer. Read the abstention rate beside it or the figure means nothing.

Does a Bible research tool make an AI trustworthy about Scripture?

It makes it checkable, which is not the same thing. You get the Hebrew or Greek, the parsing and the lexicon entry with references attached, so a wrong reading can be caught. The reading itself is still yours to judge.

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See it work: connect Darash to the AI you already use — one URL, an ordinary sign-in, 45-day free trial; the step-by-step for Claude, ChatGPT and Cursor takes five minutes. Or read how Darash compares to Logos, Accordance and Blue Letter Bible before you connect anything.

Written by Jørn André Halseth, founder of Publifye AS — ten years publishing Bibles and building the presses they run on, publisher of 4,268 Bible editions, and the author of Darash, Junifye, Lexifye and Tringine. About · Contact