Lovable Labs PlatformWhat is the AI Hallucination Risk Score?
| What it answers | How likely the answer is to be made up. |
|---|---|
| How the answer is produced | Models invent most confidently where training data is thin, contested or time-sensitive. |
| What you need to enter | Describe the task and whether you are supplying source material. |
| Where it stops being reliable | Risk bands are heuristic; a low-risk task can still produce a confident error. |
| Cost and sign-up | Free, runs in your browser, no account and no stored inputs. |
How is hallucination risk estimated?
Models invent most confidently where training data is thin, contested or time-sensitive. The score combines four risk factors: how recent the subject is, how specific the requested facts are, whether the answer requires citations or figures, and whether the model was given source material to work from.
Requests for named citations, statistics, legal provisions, prices and dates carry the highest risk, because a plausible-looking wrong value is easy to generate and hard to spot. Requests for reasoning over text you supplied carry the lowest, since the source is in front of the model.
The output includes a risk band and a targeted verification plan — which specific claims to check and in what order — because complete verification of everything is rarely realistic and unnecessary.
How do you use the AI Hallucination Risk Score?
- 1.Describe the task and whether you are supplying source material.
- 2.Flag any output that will contain numbers, names, dates, quotes or citations.
- 3.Verify the highest-risk claims against a primary source before publishing.
- 4.For recurring workflows, supply source documents rather than relying on model memory.
What can this tool not tell you?
- Risk bands are heuristic; a low-risk task can still produce a confident error.
- It cannot check factual accuracy, only estimate exposure.
- Retrieval-augmented setups reduce but do not eliminate invention, especially when the retrieved source is silent on the question.
Why some questions are dangerous and others are nearly safe?
Hallucination risk isn't evenly distributed across tasks; it concentrates wherever a model has to produce a specific, checkable value without being handed the source for it. A request for 'the current interest rate' or 'a citation for this claim' asks the model to retrieve something it may only have seen once, months or years ago, in training data that has since gone stale — and the model has no internal signal that tells it the answer might be outdated, so it answers with the same confidence whether the fact is current or years wrong.
By contrast, tasks that ask a model to reason over text you've supplied carry low risk because there's nothing to invent — the source is present in the context window, and the model's job is comprehension rather than recall. This is the single biggest lever available to reduce hallucination in practice: converting a recall task ('what does this regulation say') into a comprehension task ('summarise what this pasted regulation says') removes almost the entire risk surface in one step.
The four-factor model — recency, specificity, citation/figure requirement, and source availability — isn't meant to produce a precise probability, because no such number is knowable from the outside. It's meant to route effort: high-risk claims get checked against a primary source before publication, low-risk ones get a lighter pass, and the time saved by not verifying everything equally goes toward the claims that would actually cause damage if wrong.
The risk profile also changes with how the question is phrased, not just what it is about. Asking a model to confirm a premise you supplied — 'why did the 2019 regulation ban X?' — invites it to justify something that may not exist, because the question presupposes the fact and the model's job as trained is to be helpful about the frame it is given. Asking the open form — 'did any regulation address X, and if so which?' — makes the absence of an answer an acceptable output rather than a failure. That single rewrite removes a large share of fabricated citations and legal provisions, and costs nothing.
What do worked examples look like?
Asking for a statistic versus asking to summarise a report
'What percentage of small businesses use AI tools' is high risk — no source given, a specific number requested, a fast-moving subject. 'Summarise the adoption statistics in this attached report' is low risk — the number exists in the supplied text and the model only needs to extract it. Same topic, radically different exposure, purely because of whether the source was provided.
A legal question flagged for mandatory verification
A request for 'the notice period required for terminating a UK commercial lease' scores high risk because it's a regulated, jurisdiction-specific figure the model may state confidently but incorrectly if case law or statute has shifted since training. The output correctly recommends checking the current statute or a solicitor rather than publishing the model's answer directly.
A high-risk request and its safer rewrite
High risk: 'Give me three peer-reviewed studies showing remote work increases productivity, with authors and years.' This asks for specific citations from a contested literature with no source material supplied — close to a worst case, and the sort of request that produces real-looking references to papers that do not exist. Safer: paste two or three actual papers and ask for their findings summarised and compared, with any claim not present in the text marked as absent. The second version can still be wrong, but it can be checked in a minute against text you already have.
What do people ask most about this tool?
Why do models invent citations?
Because they generate text that resembles a citation rather than retrieving one. A fabricated reference is stylistically indistinguishable from a real one.
Does giving sources fix it?
It reduces invention substantially, especially when you instruct the model to answer only from the supplied text and say so when the answer is absent.
Which topics are riskiest?
Anything recent, regulated, numerical or niche — current pricing, law, medicine, statistics and small-company facts.
Which related tools should you try next?
Written and reviewed by Jim Vernon, Editor, AI Intelligence International. Published by AI Answer Engine, a service of AI Intelligence International, and checked against our editorial standards.