LLM Hallucinations – Definition, Examples and Potential Remedies

According to a 2025 McKinsey survey, 92% of employees in media and entertainment believe GenAI will have a net benefit in the next 5 years. Meanwhile, Google reconfirms that AI investments remain a top priority for the rest of 2025 and beyond.  Reliance on large language model (LLM) technology is becoming more common across many industries, but it often comes with many different examples of hallucination associated with it. What are LLM hallucinations, what causes them and how can their effects be mitigated in LLMs leveraged by a variety of industries?

With the rise in popularity of ChatGPT, Bing, Bard and other AI-based tools in the last two years is it really that surprising that many industries have seen a marked uptick in generative AI development?

Chief among these is the introduction of LLMs, which makes it easier for many organizations to quickly reap the benefits of AI thanks to technologies like LangChain or approaches like soft prompting – just look at some of these AI in healthcare examples.

However, before they get ahead of themselves, organizations need to remember a very apt saying for the times they find themselves in – “everyone makes mistakes.”

AI is a powerful tool after all, but it is not perfect. Therefore, organizations need to take this into account before they begin any kind of LLM Implementation, whether it be for design creation, content generation or some other task. These mistakes are known as LLM hallucinations, but what does this mean exactly, and what are some possible examples of, and fixes for, this problem?

LLM hallucination – definition

An LLM hallucination refers to anything that a program like ChatGPT, Bing, Bard or others generate that is otherwise nonsensical or detached from reality. These hallucinations occur due to faulty data being present in the LLM’s training material or in the library it is drawing from.

The following section will dive more into the causes of these hallucinations, however, the problem here for organizations should be self-evident. Any examples of LLM hallucinations could lead to a bias within the AI system itself, or false information being presented as factual.

Worse, these hallucinations can even cause LLMs to produce discriminatory content from mundane prompts. The question is then – what exactly causes LLM hallucinations?

Causes of LLM hallucinations

Of course, it is important to note that the technology behind ChatGPT, Bing and Bard continues to evolve and, as such, more causes for hallucinations may be found as time goes on, but for now this article highlights five common examples of LLM hallucination below:

• Source-reference divergence – appears when an AI system is allowed to gather data for itself with minimal human intervention, enabling it to draw the wrong conclusion from the data it collects,
• Exploitation through jailbreak prompts – occurs when individuals manipulate flaws in AI systems’ reasoning methodologies to produce outputs that are either unexpected or go way beyond the program’s intended use,
• Reliance on incomplete or contradictory datasets – happens due to the initial dataset the system is trained on containing incomplete, contradictory or outright false information, which leads to confusing or inaccurate outputs being produced by the LLM,
• Overfitting and lack of novelty – appears when the system produces outputs almost identical to the information it was trained on, while being unable to produce any answers outside what it already knows through its own reasoning,
• Guesswork from vague prompt inputs – occurs due to insufficient information being provided by the user, causing the system to fill in the gaps by itself, leading to outputs that are inaccurate or nonsensical.

Now that their origins have been discussed, take a look at some common examples of LLM hallucinations.

Types of LLM hallucination

As stated before, LLMs will continue to get smarter over the years, leading to more complex hallucinations down the line. But, for now, this article will draw attention to the five most popular examples of LLM hallucinations that are slowing down implementation efforts. These hallucination types are:

• Sentence contradiction – occurs when the dataset used to train a system is faulty, leading to an organization’s content arguing against its own stance on a given topic, issue or trend in places, making it nonsensical and harder for readers to understand,
• Prompt contradiction – materializes when outputs do not match the inputs given by users, which in turn highlights the lack of complexity in LLM models overall, resulting in a lack of trust by all users if any contradictions are found,
• Factual contradiction – occurs when datasets are incomplete or inaccurate and results in, potentially, the worst outcomes for any organization, especially as these false outputs often present themselves as factual. This might lead to organizations losing trust with their customers if these hallucinations are not caught in time,
• Nonsensical output – occurs when inputs are too vague or there are gaps in the dataset which in turn highlights the lack of complexity in LLM models overall, resulting in a lack of trust by all users – especially if they are being used in practical, day-to-day operations,
• Irrelevant or random LLM hallucinations – materializes when inputs are vague or datasets are disorganized, resulting in outputs that do not relate to either the user’s input prompts or the output they desire. This leads to a lack of LLM confidence across the board due to its perceived inaccuracy and unreliability.

How to prevent LLM hallucinations?

So far, this article has outlined the causes and examples of various types of LLM hallucinations, but how can organizations prevent them from occurring in the first place or, failing that, mitigate their presence in any LLM system?

Firstly, limiting response lengths and having more control around inputs by implementing suggestions on the home page, rather than leveraging a free-from input box, can help here. Doing this will limit the chances of nonsensical or irrelevant responses from any LLM and provides better instruction to the system, leading to better overall answers.

Adjusting model parameters and leveraging a modulation layer within an LLM is also an option, which ensures a balance between creativity and accuracy while filtering out any inappropriate responses quickly and easily. Establishing feedback from users is also critical in order to fix the areas that need the most improvement at speed. In addition, augmenting both its training and overall dataset with industry-specific information also helps reduce the chances of users encountering any more examples of LLM hallucination while using the system.

Finally, incorporating an LLM with an external database or leveraging contextual prompt engineering is also a viable solution for minimizing LLM hallucinations. However, both options come with their own sets of risks, so developers are advised to do their own due diligence on the quality of information that will drive both options forward before committing to them. Otherwise, they could easily end up with even more examples of LLM hallucinations than they ever had before.

Advanced hallucination mitigation techniques 

New methods for mitigating LLM hallucinations have evolved beyond simple fact-checking to creating dynamic, self-correcting systems. These innovative solutions focus on making the AI an active participant in the verification process, rather than a passive text generator. 

The evolution of Retrieval-Augmented Generation (RAG) is a key development. Instead of a single database lookup, Active RAG models now perform multi-step reasoning. If the initially retrieved data is insufficient, the LLM autonomously generates follow-up queries to cross-reference information from multiple sources before composing a final answer. Furthermore, many systems are shifting to graph-based RAG, which grounds the LLM in structured knowledge graphs. This allows the model to understand relationships between facts, leading to more contextually accurate and logically consistent outputs.

Internally, models are being trained with Constitutional AI principles, which include a built-in “critic” that evaluates outputs against factuality rules. If the model detects a likely hallucination in its own draft, it is prompted to self-correct or explicitly state its uncertainty. This is complemented by process-based techniques like Chain of Verification (CoVe). Here, the LLM is forced first to generate a series of verification questions about its intended answer, execute searches to answer them, and only then produce the final, verified response 

Practical use of LLM and the effects of digital transformation in 2025 

LLMs have evolved from general-purpose tools into specialized co-pilots integrated into core industry workflows, serving as a primary driver of digital transformation in 2025. They are automating complex cognitive tasks and creating significant efficiencies. 

Financial services

LLMs power Wealth Management Co-pilots that assist financial advisors. Before a client meeting, the co-pilot analyzes the client’s portfolio, scans real-time market data, and synthesizes the firm’s internal research. It then generates a concise, personalized briefing highlighting risks, identifying investment opportunities aligned with the client’s goals, and even drafts a summary email. This transforms hours of manual research into a minutes-long review, enabling advisors to manage a larger client base more effectively. 

Telecom

In network operations, LLMs function as AI-powered NOC Analysts. When a network fault occurs, the LLM instantly ingests and analyzes thousands of technical logs and historical incident reports. It identifies the likely root cause, summarizes the issue in plain English for engineers, and recommends a specific remediation procedure from technical manuals. This drastically reduces the Mean Time to Resolution (MTTR) for outages, significantly improving network reliability. 

Biotech & life sciences  

LLMs are accelerating drug development as Clinical Trial Documentation Automators. Trained on vast libraries of biomedical research and FDA regulations, these models can draft large sections of a new clinical trial protocol, patient consent forms, and final study reports. By synthesizing complex scientific data into compliant, structured documents, they cut down the administrative process by months, speeding up the time-to-market for new therapies. 

Media & entertainment

Streaming services now use LLMs for Hyper-Personalized Content Curation. Moving beyond simple genre-based recommendations, these models analyze the themes, dialogue, and emotional tone of the content a user watches. This allows for highly specific suggestions, such as, “Because you enjoyed the philosophical dialogue in a particular film, you might like this specific episode of a series that explores a similar theme.” This deep, thematic personalization significantly increases user engagement. 

What do B2B users expect from large language models? 

It’s safe to assume B2B users perceive LLMs less as novelties and more as specialized co-pilots integrated into their daily workflows. The initial hype has been replaced by a pragmatic view of LLMs as powerful productivity tools that augment, rather than replace, human expertise. Trust remains a key concern, with experienced users maintaining a healthy skepticism and verifying outputs for accuracy. Adoption has moved from generic chatbots to embedded AI within core business software like CRMs and analytics platforms. Daily operations commonly involve using LLMs to draft personalized client emails, summarize lengthy reports, query data using natural language, and generate first drafts of marketing copy. 

According to McKinsey’s latest B2B Pulse Survey of B2B decision-makers, 19 percent of respondents have already implemented generative AI use cases for B2B buying and selling, while another 23 percent are in the process of doing so. 

Legal regulations and ethical issues concerning LLMs 

In 2025, regulators in the US, Canada, and Europe focus on accountability, data privacy, and transparency, requiring organizations to treat LLM deployment with the same rigor as other regulated activities. 

Data protection and privacy 

The provenance of training data is a primary legal challenge. Regulations like the EU’s AI Act now require developers to prove their training data was lawfully sourced, making the indiscriminate use of scraped web data legally perilous. This has also intensified the debate over the “right to be forgotten,” as removing a person’s data from a model that has already learned from it is technically complex, yet increasingly a legal requirement. Furthermore, strict data residency laws in Europe and Canada force LLM providers to offer region-specific processing to prevent personal data from crossing borders. 

Liability and accountability

A crucial legal shift is placing liability for harmful or incorrect LLM outputs (hallucinations) on the application’s deployer, not just the model’s creator. A business using an LLM that provides faulty financial or medical advice can now be held directly responsible.  In response, regulations demand greater transparency and explainability, especially for high-risk use cases. Finally, a strong ethical and legal push exists to maintain a “human in the loop” for any significant automated decisions, such as hiring or credit scoring, to ensure accountability. 

The importance of an AI partner 

Software Mind developed a custom enterprise solution that delivers an integration layer between your legacy systems and modern technologies. To learn more, explore this article about MCP servers acting as an integration layer for enterprise AI.

Here at Software Mind, we know dealing with the various examples of LLM hallucinations outlined above can be challenging. But do not worry, we understand the benefits of LLMs, what they can do for you and how to implement them in your company at speed. Our proven generative AI services team is happy to talk about what you can do with your data wherever you are – just get in touch with us via this form.

FAQ 

What are hallucinations in LLM? 

A hallucination in an LLM occurs when the AI generates factually incorrect, nonsensical, or entirely fabricated information, yet presents it with confidence as if it were true. This happens because LLMs are not databases of facts; they are sophisticated pattern-matching systems designed to predict the next most plausible word in a sequence based on their training data. They don’t truly understand reality or the concept of truth. A hallucination can range from a subtle inaccuracy to inventing detailed biographies for non-existent people or citing academic papers that were never written. The danger lies in how coherent and authoritative the fabricated information can sound, which makes the inaccuracies particularly deceptive. This phenomenon is a fundamental challenge in ensuring the reliability and trustworthiness of generative AI systems. 

How can LLM reduce hallucinations?  

Reducing LLM hallucinations involves several key techniques, with Retrieval-Augmented Generation (RAG) being one of the most effective. RAG connects the LLM to an external, trusted knowledge base, such as a live search index or a company’s internal documents. Before answering, the model retrieves relevant, factual information from this source to ground its response in reality, rather than relying solely on its internal memory. Another critical method is improving the quality of the training data itself, ensuring it is accurate and well-curated. Furthermore, Reinforcement Learning from Human Feedback (RLHF) is used to fine-tune models, a process where humans rate responses, effectively teaching the AI to prioritize factual accuracy and penalizing it for fabricating information. Finally, careful prompt engineering can instruct the model to avoid speculation. By combining these approaches, developers make LLMs more reliable information synthesizers and less like imaginative storytellers. 

Do larger LLMs hallucinate less? 

Larger LLMs tend to hallucinate less on topics they are well-trained on, but the relationship isn’t straightforward. A larger model, with more parameters, has a greater capacity to memorize factual information and understand complex patterns from its training data. This often leads to higher accuracy for common knowledge questions. However, this same capacity can make their hallucinations more dangerous. When a larger LLM does hallucinate, especially on obscure or niche topics, the fabricated information can be incredibly detailed, coherent, and presented with higher confidence, making it more difficult for a human to detect. While increasing model size is one way to improve factuality, it doesn’t solve the fundamental problem. The model is still a probabilistic text generator, not a deterministic truth engine, highlighting the importance of grounding techniques like RAG, which are critical for ensuring accuracy, regardless of the model’s size.