"Despite their impressive capabilities, LLMs are not without limitations. One of the most significant challenges is the problem of hallucination, where an LLM generates factually incorrect or misleading information that appears plausible. This is particularly problematic in domains requiring high factual accuracy, such as healthcare, finance, and legal applications. To mitigate hallucinations and enhance the reliability of LLM outputs, Retrieval-Augmented Generation (RAG) has emerged as a powerful technique. RAG works by dynamically retrieving relevant information from an external knowledge source (such as a knowledge graph) at inference time, rather than just relying on pre-trained knowledge. This approach ensures that the model has access to up-to-date and accurate data, grounding answers in verified information rather than generating content purely from its internal representations." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)
"Generative AI tools for coding are sometimes inaccurate. They can produce results that look good but are wrong. This is common with LLMs. They can write code or chat like a person. And sometimes, they share information that’s just plain wrong. Not just a bit off, but totally backwards or nonsense. And they say it so confidently! We call this 'hallucinating', which is a funny term, but it makes sense." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"LLMs are trained on large volumes of data, which inherently provides them with an immense knowledge base and understanding of different languages. Yet, LLMs at their core are complex text completion engines. Since this knowledge and understanding of language is compressed in a very high-dimensional latent space. LLMs end up using these in a very fluid and intelligible way (which often leads to hallucinations). In order to guide LLMs to focus on specific topics or pieces of information to solve certain tasks, (for instance, question-answering from a given piece of text), it is important to provide contextual information explicitly. While most current generations of LLMs have extremely wide context windows, it is recommended to preprocess context into overlapping smaller chunks for better results, reduced latency, and so on. For similar reasons, it is also recommended to preprocess contextual information in clear and task-specific formats. This aspect of context preprocessing is extremely useful in Retrieval-Gugmented Generation (RAG) scenarios." (Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)
"A 'hallucination' in the context of generative AI refers to the phenomenon where a model produces information that is factually incorrect, nonsensical, or not grounded in its input data or pre-existing knowledge. These are not mere typos or minor inaccuracies; they are confident, coherent, and often persuasive fabrications. In high-stakes domains like healthcare, law, or finance, a single hallucination can have severe consequences, eroding user trust and leading to catastrophic decision-making. While all LLMs are prone to this, the RAG architecture is specifically designed to combat it by tethering the model’s output to an external, verifiable knowledge base. Understanding why hallucinations occur is the essential first step to building more reliable and truthful AI systems." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)
"Another big problem is model hallucinations, which happen when generative models make content that seems real but is actually wrong or made up. For instance, a language model could write a news story or a medical diagnosis that has wrong information. This happens because these models value coherence and fluency more than factual accuracy. When the training data is not enough or is not clear, they often 'fill in the gaps' with made-up information." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)
"Beyond intentionally misleading content, GenAI systems can produce inaccurate information unintentionally. LLMs are prone to hallucination, generating plausible but false statements with the same confidence as accurate ones. In enterprise contexts, this poses particular risks: an AI assistant might report incorrect financial figures, fabricate customer details, or misrepresent historical trends. Organizations deploying GenAI must implement validation mechanisms, human oversight, and retrieval-augmented approaches that ground model outputs in verified data sources." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)
"Ensuring that a language model reliably retrieves and presents correct information, often referred to as factual recall, is critical for any production-grade application. Whether you’re building an internal helpdesk assistant, a medical Q&A system, or an automated compliance auditor, users expect concise, accurate answers that align with up-to-date source material. Unfortunately, without explicit context, even the most powerful LLM can hallucinate or omit key facts." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)
"GenAI models are remarkably powerful, but they carry an inherent limitation rooted in how they are built. Every foundation model has a training cutoff, a point in time beyond which it has no awareness of world events, product changes, regulatory updates, or organizational developments. This knowledge gap is not a flaw that can be patched with better prompting. It’s a structural consequence of how models are trained, and it grows wider every day the model remains in production without retraining. Combined with the tendency to hallucinate when asked about topics outside their training distribution, models operating on stale knowledge can confidently deliver responses that are factually incorrect, dangerously outdated, or simply no longer relevant to the user’s context." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)
"Generative artificial intelligence (GenAI), powered by large language models (LLMs) like Google’s Gemini and OpenAI’s GPT, has transformed how we work and live, revolutionizing business after business. Despite this success, generative AI falls short in domains where specific domain knowledge, high accuracy, and explainability are essential. And it has other significant limitations, including hallucinations and a lack of context and relations. This is where knowledge graphs (KGs) come in, provid-ing contextual information - such as experiences, environmental characteristics, cultural aspects, and social normsneeded to build the 'third wave of AI' for mission-critical applications." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)
"KGs are sophisticated graph structures that represent real-world entities (people, places, diseases, proteins), define meaningful connections between them, and provide context. KGs provide structured, explainable knowledge representation but are challenging to build and query; LLMs offer natural language processing capabilities but suffer from hallucinations, stale information, and a lack of domain-specific grounding. Together, they are a 'killer combination': LLMs can extract entities and relationships from unstructured text to build KGs more efficiently, providing more autonomous and powerful graph querying and analysis. Meanwhile, KGs provide reliable, up-to-date domain knowledge to ground LLM responses and prevent hallucinations." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)
"[...] LLMs raise serious concerns about ethics, bias and fairness, errors in reasoning, hallucinations, and misuse (e.g., misinformation and disinformation). These concerns are exacerbated by modern LLMs being both literal and figurative 'black boxes': Literal black boxes because many advanced AI systems are proprietary and the weights (trained parameters of the models) are not released to the public; and figurative black boxes because even the open-source AI models are so complicated that understanding them and developing safety guardrails has thus far proven extremely difficult." (Mike X Cohen,"50 ML Projects To Understand LLMs", 2026)
"Misinformation is false or misleading information, and its generation by AI is particularly dangerous because of the aura of credibility these systems can project. In a RAG system, misinformation primarily arises from two failure points: Retrieval of inaccurate content from the knowledge base and fabrication or distortion by the large language model (LLM) during generation, even when given good context. The first line of defense is ensuring the integrity of the knowledge base. A RAG system is only as reliable as the documents it has access to. If non-credible, manipulated, or satirical sources are ingested, the system will retrieve and use them as fact. This makes rigorous data curation and source validation the most critical step in combating misinformation. The second line of defense is strengthening the connection between retrieval and generation to prevent the LLM from 'going off script'. The LLM, based on its pre-trained knowledge, might confidently generate an answer that contradicts the provided evidence or adds unsupported details – a phenomenon known as 'hallucination'. To mitigate this, thesystem must be designed to strictly adhere to the retrieved context." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)
"RAG applications must be built with semantics, metadata, and governance in mind. The retrieved information must be high-quality, secure, and appropriate for the user’s role. Equally important is monitoring and management: checking whether source data has changed, ensuring vector stores remain accurate, and watching for hallucinations or data leakage. Organizations are definitely starting to experiment with RAG models today; some are putting them into production applications. Some believe that using RAG helps mitigate hallucinations because it is grounded in trusted organizational data." (Fern Halper, "Data Makes the World Go 'Round", 2026)
"RAG models offer several advantages over standard generative models, addressing many of their limitations. Traditional generative models, like GPT, rely solely on patterns learned during training, which can lead to issues such as factual inaccuracies, model hallucinations, and lack of contextual relevance. These models generate content based on pre-existing knowledge, often without the ability to verify or update the information, making them less reliable for tasks requiring high accuracy. In contrast, RAG models integrate retrieval mechanisms that allow them to access external knowledge sources in real time. This ensures that the generated content is grounded in verified data, significantly improving factual consistency and relevance. For example, while a standard generative model might produce a plausible but incorrect answer to a factual question, a RAG model can retrieve and incorporate accurate information from a trusted source, reducing errors." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)
"Retrieval mechanisms are essential for addressing some of the key limitations of traditional generative AI models, such as factual inaccuracies, lack of context awareness, and model hallucinations. While generative models excel at creating coherent and fluent content, they often struggle to produce outputs that are factually correct or contextually relevant. This is because these models rely solely on patterns learned during training, without access to real-time or external information. For example, a generative model might generate a plausible-sounding but incorrect answer to a factual question, as it cannot verify the accuracy of its response." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)
"The foundational premise of RAG is that the most effective way to reduce hallucinations is to provide the LLM with the correct, explicit information needed to answer a query, thereby minimizing its need to rely on fallible parametric knowledge. Therefore, the quality, relevance, and accuracy of the retrieval step are the most significant factors in determining the truthfulness of the final output. Better retrieval is the most powerful antidote to hallucination. If the retriever fails to find the correct information, the generator is essentially left to guess, making hallucinations almost inevitable. The goal is to create a tight, unambiguous link between the user’s question and the evidence in the knowledge base." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)
