AI hallucination: how to detect, reduce and prevent it

A lawyer based in New York filed a legal brief that included citations to court cases that did not exist. The AI model generated those cases, providing particular case numbers and the judges' names. It displayed the fabricated details with the same confidence as the genuine citations in the brief. The legal team failed to check the data since the invented sections seemed authentic. This example demonstrates the main problem with such models. The software is not overtly lying or guessing. It carries out its programmed task, yet that task is not intended to guarantee factual correctness.

AI hallucination happens when a model delivers false data with the same fluency and confidence as correct data. This occurs because models are trained to predict plausible text rather than to check facts. You can lower this risk by grounding the model in retrieved evidence, limiting its operational scope, and checking outputs before release. Nevertheless, these approaches do not completely remove the risk.

AI hallucination occurs when a model gives incorrect information with the same coherence and organisation as correct data.

The way these systems are trained leads to this behaviour. It is not due to a programming mistake. These systems aim to predict the most probable next word rather than to verify facts against external reality.

A model will give an answer even if it has no source information. The system is built to produce responses, and a grammatically correct sentence with wrong data looks identical to one with correct data.

Shipshape Data develops data and AI systems. Hallucination is the leading cause of pilot project failures, especially when a system is prepared for customers or regulators. This guide describes why models produce incorrect answers with high confidence. It outlines how to ground a model in retrieved evidence to lower these errors, although the method does not fully eliminate them. It discusses why limiting the scope of a task is as crucial as the technology employed. The text also clarifies what citations demonstrate and what they do not. It gives instructions for creating a verification step before a system provides an answer, how to measure factuality accurately, and how to design an interface that lets users verify answers themselves.

Why the model sounds sure when it is wrong

Large language models are trained to forecast the most probable next word given the preceding text. This objective yields writing that is highly fluent. However, it does not build a system for checking claim accuracy, because the training process does not prioritise truth. The model extracts linguistic patterns from a large dataset. In doing so, it learns the exact format of how "a court case is cited like this" without assessing whether the cited case actually exists.

There is no internal fact-checker

People frequently assume a model has a memory it can consult, like a person visualising a particular meeting before describing it.

It does not work like that.

If you pose a question to a model that its training data does not cover, it will not default to saying "I don't know". Instead, it generates the most statistically likely continuation of the text. Structurally, a particular incorrect number is as plausible as a correct one. The model does not carry out a separate verification step for the number against a source, since no source is linked to the sentence during generation. The model is composing text instead of recalling facts, even when the output seems like recall.

Confidence is a writing style, not a signal

This point often confuses users. A model uncertain about a fact does not employ more cautious language than a certain model, unless it has been specifically trained or prompted to do so. The processes that generate fluency and accuracy are identical, and these two qualities do not correlate. You cannot judge the truth of an answer by its tone. This contrasts with how people interpret confident human speech. It explains why a false statistic looks identical in style to a true one within the same paragraph.

Grounding: giving the model something to point at

The most effective structural fix is retrieval-augmented generation, commonly abbreviated as RAG. Rather than asking the model to answer from its training knowledge, you retrieve the relevant documents at query time, present them to the model, and request an answer based solely on that material. Our longer article on vector databases discusses the retrieval infrastructure in more detail, but the core principle is simple: a model shown the answer is far less likely to fabricate one.

Retrieval reduces the problem, it does not remove it

Grounding narrows the range of information the model considers, delivering most of the benefit. It does not completely eliminate errors. A model may still misinterpret a retrieved passage, merge two documents into a claim that neither supports, or give a confident answer based on a text segment that matches the question wording but does not contain the actual answer. The quality of retrieval relies on the search mechanism. If an index employs embeddings that have not been tested with real user questions, it will return passages that seem relevant but are not. If the retrieval stage supplies incorrect data, the generation stage will produce incorrect information presented as grounded fact.

What actually goes wrong in practice

Common failures are often simple. If a document is poorly split, a table header may end up in one fragment while its data is in another. The model then links the incorrect header to the incorrect row. In other instances, an outdated policy stays in the index because it was not removed after being replaced. The model retrieves and cites this old version accurately, yet the resulting answer is outdated. Retrieval addresses the problem of model hallucinations. It does not repair a document store that is disorganised or outdated. Teams sometimes deploy RAG systems expecting the technology to compensate for poorly maintained content.

Constraining the brief: the model should be allowed to say no

Grounding defines the information a model can access. It does not decide which tasks the model should attempt. This second issue is a separate, less costly problem to address, though it is often overlooked.

Scope is a design decision, not a technical limit

When a model is left open-ended, it tends to try to answer virtually any question, including ones that its source material does not address. This occurs because answering is the default action, whereas refusing to answer must be deliberately programmed. You can address this by including a refusal clause in both the system prompt and the product. You ought to explicitly specify that when the retrieved context lacks the answer, the system must acknowledge that. This step is frequently omitted because a system that sometimes says "I don't have that information" seems less impressive in a demo than one that always supplies an answer. Numerous design choices are based on the product's performance in these demonstrations.

Narrow the domain, narrow the risk

A support assistant confined to a single product line will hallucinate less than an assistant that spans an entire company. The model's capability does not increase, yet the potential number of incorrect answers drops. Even though it is simple, this approach reliably improves performance. Each extra domain added to the instructions creates more chances for the model to produce confident yet incorrect information. People seldom weigh the cost of these errors against the convenience of a single assistant handling all topics.

Citations and why they need checking too

Asking a model to cite its sources appears to be a full solution to the issue. While this practice is useful, it is insufficient on its own. A model can generate a citation through the same process it uses for any other text. It can fluently write a citation without ensuring that the cited source actually supports the answer's claims.

A citation is a claim rather than proof

The Avianca case mentioned at the beginning of this guide demonstrates this failure. The citations seemed correct since the case names, reporters, and page numbers were in the right order. Yet, no real court cases existed to back them. You should treat a citation produced by a language model with the same scepticism as any other output. The process that creates a genuine citation is identical to the one that creates a fabricated citation.

A citation only confirms a source's existence when it has been verified. Without verification, it is just a statement that seems factual.

Features of a reliable citation pipeline

A reliable version connects each citation to a specific passage retrieved from a document. It highlights the exact text span rather than letting the model generate a reference from memory. If the system cannot trace a citation to a document it actually retrieved, it must not show that citation.

This task demands substantial engineering. You need to track provenance across the whole pipeline, from the original chunk to the final answer. This guarantees that a citation such as "source 3" in the output acts as a traceable pointer to a real document.

Building a verification step before the answer ships

Grounding and citation happen before the model produces a response. Verification occurs afterwards. Teams frequently skip this final step when under tight deadlines. Verification adds latency and is less visible in a demo than a fast, confident answer.

Automated tools detect basic issues

The cheapest method is a rule-based check. This process checks whether each number in the answer appears in the retrieved context and whether each named entity matches the source. These checks do not catch subtle misreadings. Nevertheless, they are effective because many hallucinations are simple errors, like completely invented figures, rather than complex misinterpretations of real data.

A second pass, done properly, is worth the latency

A more detailed approach runs a second model pass to verify the initial answer against the retrieved sources. This second pass flags any information not supported by the sources. This process raises costs and adds latency, which may be unsuitable for some use cases. Nevertheless, this step is typically required for official answers, report figures, claims in regulated documents, or statements that customers depend on. The most common mistake teams make is adding this stage at the end of development or leaving it out entirely. They view it as an optional improvement, yet this step actually decides whether the system is safe to release.

Evaluating for factuality, not fluency

Most evaluation methods for language models assess whether the output is fluent, relevant, or generally on topic. Factuality needs a separate test. A model can achieve high scores for fluency and relevance even when it supplies incorrect specific data.

Create a test set containing known answers

We create evaluations for clients by assembling a set of questions with human-verified answers drawn from the specific domain where the system will operate. We run these tests whenever the model, the prompt, or the retrieval configuration is changed. Generic benchmarks indicate average model performance, but they do not reveal whether the system will correctly handle your specific product policies or contract clauses. This specific accuracy is what matters when the system is in production. Our guide on using OpenAI Evals to test and tune LLMs describes a practical method for this type of testing.

Track the rate, not the anecdote

When a stakeholder spots a single incorrect answer, the response is often disorganised and lacks proper measurement. The key metric is the error rate. You need to determine how many answers in a representative sample contain unsupported claims and monitor whether that rate changes as you adjust the system. Without a specific metric, every adjustment is speculative and regressions stay undetected until a customer reports them.

Designing the interface so the user can check

Even well-grounded and verified systems will still make occasional mistakes. The previously mentioned methods do not remove all errors. No model possesses the intelligence needed to make this step optional.

The final security measure consists of an interface that asks users to verify the answer while they use the product.

Show the source alongside the answer

The simplest approach puts the retrieved passage right next to the answer rather than concealing it behind a link. Most users will not look for evidence when they must navigate through several pages. In such situations they will accept a confident answer even when it is wrong. When evidence is shown immediately, users can check the information with a quick glance.

Let uncertainty show

A system that detects low-confidence retrieval, insufficient source coverage, or queries beyond its scope is more dependable than one that keeps a constant tone of certainty. This practice is connected to the broader field of responsible AI. It is a specific practical application that gives enough information for a user to verify a particular answer.

Make correction part of the loop

Give users a simple way to flag incorrect answers and forward those reports to a human reviewer. This process has two purposes. It identifies errors that passed previous checks, and it helps build the evaluation set mentioned earlier. A flagged error together with the correct answer supplies the specific test case needed to improve the next version of the system. Teams that omit this feature treat each hallucination as an isolated incident and often see the same failures repeatedly.

Where to start

Do not start by searching for software that detects hallucinations. Instead, define precisely what information the system may access and program it to reject any requests beyond those limits. This step reduces risk more effectively than most technical solutions and only needs a clear decision about the project's scope.

Link the system to reliable data. Use content that is regularly updated, add citations that point to specific passages, and verify all information before it reaches the user. Create a small test set from your own data to monitor accuracy over time. These methods do not need complex engineering. They need a level of discipline that is often ignored during tight deadlines, which is why you should apply them correctly from the start.

If you are building an AI system that must be trusted with an answer someone will act on, and you are not sure it would pass a source check, talk to us. We would rather help you find the gaps before a customer or a regulator does.

Many teams reach this point in theory but stall at the build. Our AI development service is designed for exactly that step.

To see how these controls work in a support assistant, including grounding and escalation, read what a bespoke AI chatbot should include.

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