What is an AI hallucination, and can it be eliminated?

An AI hallucination is an incorrect answer delivered with confidence by a language model, which invents a fact, a source, or a figure [34]. Most of the time, nothing in the tone signals the error. Some definitions are broader: they include any inaccurate, misleading, or inconsistent output [37], or any false information produced by a generative AI model [18].

Can they be eliminated?

No: to date, total elimination is not possible [33]. Several elements converge:

• A theoretical finding. According to an article in Monde Numérique, theoretical work concludes that these hallucinations are unavoidable and cannot be fully eliminated, regardless of architecture or training data [40].
• An origin tied to how the models work. The capacity that makes models inventive also sometimes produces a blend of facts and inventions. They are therefore not simple bugs [24].
• No single technique is sufficient. According to Databricks, a data platform publisher, no single technique eliminates them [31].

What can be done, then?

They can be strongly reduced [40]. This involves layering safeguards rather than relying on a single solution: solid data practices, clear system limits, continuous testing, and human oversight [31]. Another guide lists seven techniques: RAG (retrieval-augmented generation), forced citations, the "according to X" prompt, question decomposition, low temperature, Chain-of-Verification, and human validation. It specifies that none fully eliminates the risk and that human verification remains necessary [32].

The following sections detail these safeguards, from preparing the question to verifying the answer.

Why does an AI invent answers?

An AI invents because it produces plausible text rather than verifying facts, and several factors heighten this risk. Five typical causes are documented: lack of context, outdated data, vague prompts, training biases, and the model's overconfidence [16].

Data also plays a role: hallucinations appear when the AI lacks structured, reliable, and usable data [21]. A guide for IT directors specifies that a contradictory or outdated document provided as a source is an almost certain cause of error, since the answer cannot exceed the quality of what it is given [28].

Why inventiveness and error are linked

Hallucinations are not simple bugs. The capacity that makes models inventive is the same one that sometimes produces a blend of facts and inventions [24]. This finding concerns widely used tools, including ChatGPT and Gemini [24]. Fixing just one of the causes above is therefore not enough to make the phenomenon disappear.

A caveat about these sources

These causes come mainly from websites, often written by actors who offer their own solutions, rather than from peer-reviewed studies. They describe frequently cited factors without measuring the weight of each.

How can hallucinations be reduced before even asking the question?

Four levers act upstream, from most structural to simplest: anchoring the model in your documents, curating those sources, structuring the prompt, and lowering the temperature. They reduce the risk without eliminating it: no single technique removes hallucinations [31][32].

Anchor the model in your documents (RAG). The most effective approach is to limit what the AI must "guess" by providing your own documents as a base [13]. RAG (retrieval-augmented generation) connects the model to reliable sources before it generates its answer [30]. It is among the techniques recommended by several guides [10][11][25][32]. A specialized site claims that with a well-configured RAG, the rate of factual errors would generally fall between 1% and 3% [28]. It takes this figure from a third-party article without measuring it itself: it is an indication, not a guarantee.

Curate the quality and date of sources. A contradictory or outdated document is an almost certain source of hallucinations, and the answer cannot exceed the quality of what is provided [28]. Structured, reliable, and usable data limits errors [21].

Structure the prompt. A well-built prompt reduces risks [30]. The techniques cited are step-by-step reasoning, the "according to X" prompt, and breaking the question into sub-questions [32]. An instruction can thus ask the model to reason step by step and prioritize reliable sources [39]. Prompts that account for uncertainty are also among the suggested approaches [23].

Lower the temperature when the tool allows it. Through an API, a temperature of 0.1 to 0.3 reduces creative responses, and therefore hallucinations [27]. This setting is presented as the foundation of mitigation strategies [11]. Many chat interfaces do not expose it.

Why ask for cited sources, and what are they worth?

Asking for cited sources makes an AI answer verifiable: instead of a claim to take on trust, you get elements you can check against the original. For uses where factual accuracy is critical, the model can be required to cite its sources precisely: document name, page, URL [10]. "Forced citations" are among the seven practical techniques identified against hallucinations [32]. Displaying the sources used to generate an answer is also a simple practice that builds trust [28].

A cited source is not a verified source

A citation can itself be invented: a model can fabricate a fact, a figure, or a reference [34]. According to one source, 30% of citations generated by chatbots are hallucinated [32]. You must therefore open every link and every cited reference, since that is where invented sources are found [38]. In practice:

• check that the link opens and leads to the announced document;
• search for the exact title of a study or article, and check it on Google Scholar or Semantic Scholar for academic publications [32];
• read the relevant passage to confirm it actually says what the answer attributes to it;
• cross-check high-stakes facts against a reliable primary source [38].

Even with citations, no solution fully eliminates hallucinations: human verification remains necessary [32].

A practice that applies to every tool

Oupi cites its sources in deep research, which makes this check easier (see chat with documents and research). But the requirement applies regardless of the tool: ask for references, then verify them one by one.

How do you verify an AI answer, step by step?

The method comes down to four actions: identify high-risk facts, cross-check them against a primary source, open every cited link, then validate manually before any publication. It applies to any tool, since a hallucination can concern a fact, a source, or a figure [34].

1. Identify high-risk facts. The more precise a piece of information is (date, figure, quotation), the more it requires verification [27]. Highlight these elements in the answer before reusing it.
2. Cross-check against a primary source. Compare each high-risk fact with a reliable primary source: official text, institution, research publication [38]. Human reviewers who compare results against reliable sources can thus detect and correct errors [26].
3. Open every link and reference. This is where invented sources are found [38]. If a link leads nowhere or does not confirm the claim, discard it.
4. Search for the exact title of a cited source. Paste it into a search engine: if it does not exist, it is a hallucination [27].

Once these checks are done, never publish a figure or quotation without manual verification [27]. No technique fully eliminates hallucinations, and human verification remains necessary [32].

This method is faster when the tool provides sources. In deep research, Oupi cites its own sources, which gives you links to open and references to check (see searching your documents). These citations remain a starting point: opening the source is what validates the answer.

How do you organize AI reliability within a team?

In a team, the basic rule is simple: the AI drafts, the human validates, corrects, and signs off. Any content leaving the company (client email, report, commercial offer, publication) must be reviewed by a competent person [13].

Layer multiple defenses. No single technique eliminates hallucinations. Good data practices, clear system limits, continuous testing, and human oversight must be combined [31]. It is better to prevent than to detect after the fact, with several layers of defense covering data, models, and governance [12]. Reviewers' feedback is then used to refine prompts, document retrieval, and internal policies. This loop reduces errors over time [12]. On the tools side, this means choosing reliable platforms, adding RAG, writing precise instructions, and providing human oversight [25].

Train and equip users. Enterprise defense includes training teams to spot an invented answer [34]. For professional documentation, one academic source recommends establishing good-practice rules and raising user awareness [5]. Another publishes recommendations for safe and critical use of generative AI [1].

Maintain critical thinking. According to one research work, critical thinking cannot simply be decreed: it requires intentional practice [9]. In practice, this means making verification a regular habit rather than an exceptional check.

Note on sources: the available excerpts of [1], [5], and [9] are short and truncated, and they offer guidance rather than a complete method. The other references come from publisher and vendor websites, which have a commercial interest in these topics.