How to reduce AI hallucinations?

Artificial intelligence today generates text, analyses documents, helps customer support and automates processes in firms. With the use of AI tools, though, one fundamental problem grows too, namely that it makes things up, or rather hallucinates. An AI hallucination arises when a model creates an answer that sounds compelling but is factually incorrect, made up or misleading.  In this article we will show the strategies that are today among the most effective ways of reducing hallucinations in AI tools.

What are AI hallucinations and why do they arise? 

Hallucinations are today among generative artificial intelligence's greatest problems. These are situations where an AI model creates an answer that comes across as trustworthy, logical and compelling but is in fact incorrect, inaccurate or entirely made up. It matters to grasp that large language models (LLMs) do not work like classic databases or search engines. A model does not look up "the truth" in a database of facts. Instead it predicts the most likely next word or sentence on the basis of the enormous amount of data it was trained on. In other words, AI does not generate answers on the basis of genuine understanding of the world but on the basis of statistical probabilities.

One of the main causes of hallucinations is outdated or poor-quality data. Another frequent reason is gaps in the model's knowledge. No AI model has perfect coverage of all subjects. A great influence on hallucinations arising is also had by ambiguous prompts. If a user gives too general or imprecise a request, the model can interpret the context wrongly. For instance a question along the lines of:

"Write me an analysis of the firm Invelity."

can lead to various interpretations. The less precise the prompt, the higher the risk of inaccuracies.

How often do AI tools hallucinate?

According to research, an important finding for 2026 is that there is no single universal "AI hallucination percentage". Various benchmarks, after all, measure different types of failure: for instance whether a model stays faithful to a source document, whether it prefers to guess instead of admitting uncertainty, whether it cites sources properly or whether its claims are genuinely backed up. This difference matters extremely. On controlled tasks such as summarising text, the best models can come across as very reliable. On more demanding scenarios, for instance legal questions, medical tasks, verifying citations or multi-stage research workflows, the error rate rises markedly. That is exactly why mechanisms for reducing hallucinations are becoming a basic part of AI architecture.

Data from AI hallucination benchmarks in 2026 shows that the problem of incorrect, made-up AI answers is much more serious than originally assumed. It is estimated that global business losses caused by AI hallucinations reached as much as 67.4 billion dollars in 2024. Although some models achieve a very low hallucination rate in controlled conditions, the reality is considerably more complicated. For instance the model Gemini-2.0-Flash achieved a hallucination rate of only 0.7% on basic text summarisation, which represents one of the best results in benchmarks aimed at simple summarisation tasks. These figures, though, do not mean the model will be equally reliable in legal, medical or enterprise scenarios. The greatest differences between models appeared in financial questions. As many as 72.1% of financial prompts triggered disagreement between AI models. 

"We see AI hallucinations as a key limit on where we can use generative AI in critical company processes, from customer support through legal and financial use cases to marketing. The key is not looking for one miracle model but assembling an architecture: combining RAG, domain fine-tuning, guardrails, agentic workflows, compulsory citations and human-in-the-loop so that AI does not answer blindly but rests on verified data and measurable reliability."

Michal Greguš, Team Leader & Co-Founder

What are the proven strategies for reducing AI hallucinations?

Retrieval-Augmented Generation (RAG)

The most widespread protection against hallucinations today is RAG. Retrieval-Augmented Generation works by having an AI model first look up relevant information from a database, documents or an internal knowledge base before answering. RAG's advantage is access to current information, a lower risk of made-up answers, the option of citing sources and greater control over content. Microsoft and AWS at the same time point out that a RAG system's quality depends on the data's cleanliness, the retrieval's relevance, properly segmenting documents and the embeddings' quality. If the knowledge base is poor, AI will hallucinate despite using a RAG architecture.

Fine-tuning on domain data

KDnuggets points out that specialised models have a lower hallucination rate than generic LLMs. If AI works in areas such as healthcare, law, finance, customer support or technical documentation, fine-tuning on quality internal data can markedly raise answers' accuracy. What matters, though, is that the data used be of good quality and consistent. Poor or outdated data, after all, can make hallucinations worse still.

A practical example

Imagine an AI assistant for a law firm that helps prepare contracts and answer clients' questions. If the system used only a generic LLM without specialisation, it could interpret legal terminology wrongly, use outdated laws or create imprecise legal formulations. Using fine-tuning on internal data, though, the model can be trained on the firm's contracts, legal templates, internal terminology, court decisions or specific compliance rules. The result is an AI system that better understands expert terminology, uses the right formulations and hallucinates markedly less on expert questions.

Prompt engineering and the ICE framework

A prompt's quality dramatically affects AI answers' accuracy. Microsoft recommends using the so-called ICE framework, consisting of three elements: Instructions (clear instructions), Constraints (precise restrictions) and Escalation (defining situations where AI is to admit uncertainty). Instead of a simple prompt:

"Answer the customer."

it is better to use:

"Answer only on the basis of the documents provided. If you cannot find the answer, say the information is not available."

Microsoft at the same time recommends repeating critical instructions at the prompt's start and end, thereby lowering the risk of the model "wandering off" while generating. 

You can take inspiration from our Invelity Antler framework too, which joins RAG, guardrails, validating answers and human-in-the-loop checks into one practical AI workflow. It is designed so that AI systems do not answer "blindly" but work with verified data, can admit uncertainty and validate critical answers before sending them to a user. You can read more about the framework and practical implementations in the article.

Agentic workflows and dynamic checks on answers

Amazon Bedrock Agents brings the concept of agentic workflows. Once an answer is generated, an AI agent dynamically decides whether the answer is reliable, whether a hallucination check needs running or whether a person needs bringing in. If the system detects a high risk of hallucination, the request is automatically escalated to a human-in-the-loop check. A great advantage is that the detection rules can be changed without needing to rebuild the whole workflow system, which matters extremely in an enterprise environment.

A practical example

Imagine an AI assistant in e-commerce that communicates with customers about products, complaints and stock availability. With simple questions the chatbot can answer automatically, but in more complicated or risky situations the system has to judge whether the answer is reliable enough. An agentic workflow works by having the AI, once an answer is generated, automatically decide whether the answer is safe, whether a hallucination check needs running or whether a person needs bringing in.

For instance a customer asks:

"Can I return a product even after damaging it in use?"

The AI model can identify that it is a sensitive question with legal or commercial impact. The system therefore runs an additional check on the answer, compares it with the current returns policy and, if it detects uncertainty or a risk of hallucination, automatically redirects the communication to the support team.

Confidence scoring and uncertainty estimation

Modern AI systems today rate not only the answer itself but the model's degree of certainty too. NVIDIA in its NeMo Guardrails solution integrates the Cleanlab Trustworthy Language Model (TLM), which analyses in real time an answer's trustworthiness, the likelihood of error and the degree of uncertainty. If the confidence score falls below a defined limit, the system can return a fallback answer, refuse to answer or bring a person in. This approach matters extremely in customer support and regulated industries.

A practical example

Imagine an AI assistant in the customer support of a telecoms firm that answers customers' questions about invoices, roaming or contract terms. Some questions are simple, but with more complicated requests the model may not be entirely sure the answer is correct. It is exactly here that confidence scoring and uncertainty estimation come into play. The AI system continuously evaluates how sure the model is of an answer, what the likelihood of error is and whether there is enough evidence for the claim it is generating. If the confidence score falls below a defined threshold, the system can refuse to answer, show a fallback message or automatically redirect the communication to a person.

Guardrails and validation layers

Guardrails represent protective mechanisms between a user and an AI model. Their task is to filter dangerous answers, check facts, prevent jailbreak prompts, check answers' consistency and ensure compliance rules. Well-designed guardrails are today essential above all with AI chatbots that communicate directly with customers. Without the right protective layers, a chatbot can give incorrect information, breach a firm's internal rules, share sensitive data or generate answers that damage customers' trust and a brand's reputation.

A practical example

A practical example is also our AI shopping assistant for Shox.sk, where it was critical to ensure the AI recommended only relevant products, worked with current data and did not create misleading answers while communicating with customers. You can read more about the implementation in the article How we created an AI shopping assistant for shox.sk

Automated logical verification (Automated Reasoning)

One of the most advanced approaches is Automated Reasoning. AWS claims it is the first guardrail from a major cloud provider that does not use merely ML prediction but mathematically formal logical verification. The system analyses claims, compares them with defined rules and logically verifies an answer's correctness. AWS uses this technology in cryptography and network security too. It is a fundamental shift compared with classic probabilistic hallucination detectors.

A practical example

Imagine an AI assistant that automatically generates ad campaigns and claims for an e-shop or a brand. The marketing team enters a request:

"Create an ad for the product with the best price on the market and a guaranteed result."

The AI can create marketing copy that sounds attractive but at the same time can breach advertising rules, internal brand guidelines or legislation. An Automated Reasoning system therefore logically verifies before the campaign is published whether the firm can genuinely use the claim "the best price on the market", whether evidence exists for a "guaranteed result", whether the copy contains forbidden or misleading formulations and whether the ad conflicts with Meta Ads or Google Ads rules. If the system finds a logical or compliance conflict, it blocks the ad, adjusts it or sends it to the marketer for checking.

Requiring citations for key claims

One of the most effective ways of reducing AI hallucinations is compulsorily citing sources for all important claims. Instead of generating answers merely "from memory", a model has to attach to every factual claim a relevant source, citation or particular excerpt of text the information comes from. If the system cannot find supporting evidence, it should admit uncertainty rather than creating a compelling-sounding but unsupported answer. Compulsory citations matter extremely today above all with legal AI systems, healthcare applications, internal knowledge base chatbots, customer support and compliance workflows, where a single incorrect answer can cause reputational, legal or financial problems.

A practical example

A practical example can be an AI chatbot in e-commerce that communicates with customers about complaints and returns. Without citing sources the chatbot can answer wrongly:

"You can return goods within 60 days."

even though the real period is only 30 days. With a properly designed system, though, the AI first looks up the current information, creates an answer only on the basis of the document found and at the same time states the particular part of the terms and conditions the information comes from. If it does not find a relevant source, it answers, for instance:

"I did not find information about this condition in the documents available."

Multi-agent validation

Ever more popular is validation through Multi-Agent Self-Verification. Instead of one model, several AI agents check an answer. One generates the answer, a second verifies the facts, a third analyses consistency and another judges safety. The advantage is greater accuracy, a more robust system and a lower risk of one critical error. The drawbacks are higher costs and greater system latency.

A practical example

Imagine an AI system that automatically prepares marketing campaigns for an e-commerce brand. The marketing manager enters a request:

"Create an ad campaign for a new product including a headline, claims and a CTA."

In a Multi-Agent Self-Verification workflow, the answer is not checked by only one model. The first AI agent creates the campaign proposal, the second agent verifies the facts and product information, the third analyses the communication's consistency with the brand guidelines and another agent checks the compliance rules of advertising platforms such as Meta Ads or Google Ads.

The system can thus uncover incorrect product claims, non-existent product benefits, misleading marketing claims or formulations that can lead to an ad being rejected.

If the individual AI models do not agree or identify a risk, the system adjusts the ad, marks the problematic parts or sends it to the marketer for approval.

Monitoring and continuous testing

Hallucinations often appear only after a system is deployed to production. So experts recommend continuously monitoring answers, analysing failures, regular evaluations, gathering user feedback and updating the knowledge base. Experts from Promptfoo at the same time point out a critical problem. Many firms implement protections against hallucinations but do not measure their effectiveness. They therefore recommend defining test scenarios, simulating typical errors, comparing various strategies and automating evaluation. The key question is:

"How to quantify an AI system's reliability?"

Without metrics, after all, AI cannot be scaled safely.

RLHF and feedback from people

Reinforcement Learning from Human Feedback (RLHF) helps models better distinguish quality answers, incorrect claims, risky formulations and unsuitable reactions. It is exactly RLHF that stands behind the marked improvement in modern chatbots such as ChatGPT and Claude.

A practical example

An AI chatbot for an e-shop's customer support that communicates daily with hundreds of customers. At the start the chatbot can generate answers that do sound natural but contain imprecise information, an unsuitable tone of communication or misleading recommendations.

Using RLHF (Reinforcement Learning from Human Feedback), though, the support team or AI trainers mark answers as correct, incorrect, useful, misleading or unsuitable.

The model then learns which answers are of good quality, what tone of communication is suitable, when to admit uncertainty and which formulations can damage the customer experience.

Human-in-the-loop is still current

Despite all the technologies, experts agree that a person remains the last and most important layer of safety. That holds above all with legal answers, healthcare recommendations, financial decisions and compliance processes. Modern AI systems therefore ever more often use a hybrid model: AI + validation by a person.

The aim is not to remove the person but to minimise the number of situations in which they have to step in.

AI hallucinations today represent one of the greatest obstacles to deploying generative artificial intelligence in company processes. AI's future therefore lies not in blind trust in models but in building intelligent, trustworthy AI solutions that can work with verified information, admit uncertainty and give consistent results even in demanding enterprise scenarios.