What is an AI hallucination in research?

In this guide, an AI hallucination is a generated statement or reference presented as factual without adequate support. It may be an invented paper, a wrong bibliographic detail, or a claim that goes beyond what a real paper reports. Fluent wording and a working link do not settle which of these problems is present.

For researchers, the useful response is to check the source and the sentence separately. Reformatting a citation cannot repair an unsupported interpretation.

What published evaluations actually found

Walters and Wilder (2023) examined 636 references in 84 short literature reviews generated on 42 multidisciplinary topics. In that study, 55% of GPT-3.5 references and 18% of GPT-4 references were fabricated. Among references to real works, substantive citation errors remained.

Those results describe the tested models, prompts and task. They are not a current error rate for all AI systems, a clinical-review benchmark, or an evaluation of INRA.

Magesh and colleagues’ 2024 preprint evaluated retrieval-based legal research tools and found that retrieval did not eliminate hallucinations. The work was published in 2025. Its legal tasks do not establish a percentage reduction or clinical accuracy rate for another product.

Why retrieval alone does not settle correctness

Finding a relevant document and using it correctly are separate tasks. A generated answer can attach a real source while changing its population, outcome, timeframe or degree of certainty. Repeating the request may change the answer; agreement between repeated answers is not independent verification.

Evaluate the output against the material actually available. If full text is missing, a tool should not be treated as having checked details available only in that full text. There is no universal verification-time estimate in this guide: the work depends on the claim, source access and the expertise required.

A fictional clinical example: a real source can still be misused

Illustration only; no study or clinical result is being reported. Imagine a source note describing patient-reported confidence after a discharge telephone call. A draft turns that into: Telephone follow-up prevents readmission.

The draft has changed the outcome and asserted an effect that the fictional note does not establish. Locating the source would not fix that problem. The reviewer needs to inspect the actual outcome, design, comparator and follow-up period, then decide whether to narrow the sentence or remove it. This example illustrates a review question, not evidence about telephone follow-up.

A practical check before you reuse an AI-generated claim

Locate the work. Match the identifier, title, authors and publication details against a publisher or bibliographic database record. If you cannot find it, keep it unresolved rather than inventing a replacement reference.
Locate the passage. Read the relevant text, table or figure and its surrounding context. Record whether you inspected the abstract, full article or a supplement.
Compare the claim with the source. Check the population, setting, design, outcome, timeframe and uncertainty. Keep quotation, paraphrase and your own interpretation distinct.
Check publication status. Look for corrections, retractions and version changes. An identifier that resolves still needs this check.
Decide and record. Retain, qualify, remove or seek specialist review. Keep the reason and source location so a co-author can inspect the decision.

This is an editorial checking aid, not a validated risk-of-bias tool, a completeness guarantee or clinical decision support. Methods and clinical interpretations may require a qualified reviewer.

For source records, Zotero’s guidance on adding and checking records also recommends checking imported metadata. For a detailed source-by-source workflow, continue to citation accuracy and verification.

What this means when using INRA

Keep the same checks when reading an INRA draft. Inspect the available sources and their support for substantive statements; request corrections where evidence is missing or overstated. This article provides no measured INRA hallucination rate, zero-error promise or publication-readiness guarantee.

Use the INRA workflow description to understand the product scope. Source availability, successful generation and scientific validity are separate questions.

Editorial update: October 19, 2026. Unsourced incident stories, fixed checking-time estimates and zero-error product promises have been removed. The example above is explicitly fictional.