Conceptual Article

An Overview of AI Hallucinations with RAG and Knowledge Graphs

Published on January 7, 2025

Adrien Payong

An Overview of AI Hallucinations with RAG and Knowledge Graphs

Introduction

AI has revolutionized the industry by automating tasks, analyzing large data sets, and assisting natural language communication. However, despite AI systems getting increasingly advanced, AI hallucination remains a persistent challenge.

This is a key issue in highly reliable healthcare, law, and banking applications. Integrating RAG and knowledge graphs is a promising way to reduce hallucinations by grounding AI systems in verifiable, structured information. This article explores AI hallucinations, the benefits and challenges of RAG systems, and their potential integration with knowledge graphs to mitigate hallucinations.

Prerequisites

You’ll need some basic knowledge about AI and generative models to follow this article. You should be well-acquainted with RAG processes and knowledge graphs. A basic understanding of graph databases like Neo4j will be useful. Understanding industry-specific challenges in areas such as healthcare and finance will be helpful. The ability to visualize data using tools such as NetworkX for knowledge graph representation is also recommended.

What Are AI Hallucinations?

AI hallucinations refer to the phenomenon where AI models produce false, illogical, or invented outputs. These results might seem rational or logical to the naked eye, but they are fundamentally corrupt.

Types of AI Hallucinations

AI hallucinations can take many different shapes, each affecting the integrity of the AI system differently. This knowledge is crucial to recognizing and mitigating their prevalence in real-world applications.

Factual Hallucinations

Factual hallucinations occur when an AI system produces outputs that contradict factual statements, usually due to inaccuracies or omissions in the training data or retrieval mechanisms. For instance, an AI might falsely say, “The Eiffel Tower is in Berlin,” even though it is a verifiable falsehood.

These errors are especially harmful in healthcare, legal services, and education, where misinformation causes adverse outcomes and damages user confidence in the system. The most common reasons are out-of-date or biased training data and erroneous information in the extracted documents.

Semantic hallucinations

Semantic hallucinations arise when an AI system generates grammatically correct but context-irrelevant or incoherent responses. If you ask, for example, "What are the symptoms of diabetes? "an AI could respond: “The first mention of the symptoms of diabetes can be found in the Ebers Papyrus.”. This is essentially correct but does not satisfy the query intent.
These hallucinations undermine AI’s usefulness for contextual tasks such as customer or technical support, making users frustrated and distrustful. Typically, they result from a misalignment between the model’s probabilistic prediction and the query intent. They can also happen because of a deficiency in semantic grounding or contextual understanding.

Reasoning hallucinations

Reasoning hallucinations occur when an AI system generates outputs whose logical conclusions are incorrect. This is usually due to misunderstanding the interrelationship between entities or concepts. For instance, if you say, “All apples are fruits” and “Oranges are fruits,” the AI might wrongly conclude, “All apples are oranges.”
These errors are particularly damaging in scientific, legal, or technical tasks where logical consistency is critical. They are caused by the lack of representation of logical relationships in training data and the lack of clear reasoning mechanisms in generative models.

Why These Types Matter

Understanding these types of hallucinations is essential for effective AI development. Factual hallucinations rely on techniques such as retrieval-augmented generation (RAG) to verify outputs against external sources.

Query refinement and context integration through knowledge graphs can help prevent semantic hallucinations. Reasoning hallucinations, by contrast, require symbolic reasoning or direct logic modules to ensure logical consistency. If AI systems address such hallucinations, they can produce more accurate outputs, serve their purpose, and gain users’ trust.

Causes of AI Hallucinations

AI hallucinations arise from data limitations, model architecture constraints, and the complexities of understanding context. Below are the primary causes:

Training Data Limitations: LLMs are often trained on large, unreliable datasets or out-of-date information. This results in misrepresenting knowledge, and models will likely fabricate details when challenged with unknown or vague questions.
Overgeneralization: Artificial Intelligence models generate outputs based on probabilistic predictions. This enables flexibility and can result in confident but inaccurate statements, especially if the model lacks domain-specific knowledge or encounters edge cases.
Absence of Contextual Awareness: Training models without access to real-time verified information can’t maintain contextually relevant and accurate outputs. This lack of grounding is especially evident in fast-paced industries like healthcare and finance, where current, specialized knowledge is critical.
Lack of Explicit Reasoning: Generative systems often lack explicit reasoning about relationships between entities or facts. This produces incorrect logical deductions or meaningless responses.

These causes emphasize the importance of using accurate real-time data for AI models and implementing methods like RAG and knowledge graphs to avoid hallucinations. Addressing these root causes allows AI systems to generate more accurate and context-aware results.

Retrieval-Augmented Generation

RAG is an AI architecture that complements generative language models with a retrieval engine. Rather than solely using the pre-trained weights, RAG searches for external documents to contextualize the model’s responses.

How RAG Work

The process starts with analyzing the query and converting it into a format suitable for retrieval. During the Document Retrieval phase, a retrieval mechanism (for example, vector search) fetches the appropriate documents from an external knowledge resource.

In response generation, the model uses retrieved documents as additional context to produce outputs based on the most up-to-date and relevant data. The image below shows how a query is transformed into an appropriate, contextualized response through embedding, retrieval, fusion, and generation.

User Query

At the top of the diagram, it starts with a user query. For example:
“What are the symptoms of diabetes?”
This query is the user-provided question or input that the AI system will go through to produce a response.

Query Understanding: Query Embedding

The Query Understanding step interprets the query input and converts it into a vector embedding. This is a numerical representation of the query in high-dimensional space. For example, the query is encoded as an embedding (vq) using an encoding function f(encode).
This embedding allows the query to be compared against other data.

Document Retrieval

To identify the most relevant documents, query embedding (vq) is compared to different embeddings (vd1,vd2,vd3…vdi) in the knowledge base. A similarity function, such as cosine similarity, ranks the documents by relevance.

Vector Space: Displaying Top Matches

This step visualizes how similar (dotted box) the query embedding (vq) is to the document embeddings (vd1,vd2,vd3,…vdi). Based on similarity scores, only the most relevant documents are displayed as the best matches.

Context Fusion

In the Context Fusion step, query embedding (vq) and embeddings of the Top-k documents retrieved will be merged into a context vector. That’s achieved through a fusion function g(vd1,vd2,vd3,…vdi) incorporating the information to add rich context. This step provides the generative model more context for an accurate response.

Response Generation

Based on the context vector from the previous step, the AI engine returns a coherent and context-accurate response. The Response Generated can look like this:
“Common symptoms of diabetes include increased thirst, frequent urination, and fatigue.”
This response solves the user request directly while grounding the output in the retrieved documents.

Knowledge Base

The Knowledge Base represents the source of external information. It contains a large collection of documents that the system uses to retrieve relevant embeddings. These documents also ensure the generated answer is supported by accurate and relevant information.

Benefits of RAG

One key advantage of RAG is that it reduces factual hallucinations. With the external knowledge extracted through document retrieval, RAG bases its outputs on real data sources instead of relying only on internal model predictions.
When asked about the latest medical treatment for a disease, RAG can find related papers or guidelines from reliable medical databases and combine them to provide a factual, reliable response. This makes it especially useful in healthcare, law, and finance, where factual precision is necessary.

The other advantage of RAG is its flexibility and adaptability. Unlike static generative models, which must be retrained to update their knowledge, it can dynamically include new and specific information through its retrieval system.
This can be especially useful for fast-moving industries such as news, scientific research, and technology, where timely information is needed. Companies can customize the retrieval system for a specific knowledge base to enhance domain expertise without modifying the underlying generative model.
Lastly, RAG adds transparency and explainability to AI systems. Using documents as input allows users to trace the source of the information provided, increasing trust in the system and enabling users to verify the facts independently.

Challenges of Retrieval-Augmented Generation

While RAG increases the scalability and robustness of generative AI systems, it also raises some challenges that need to be solved for its implementation. Let’s consider some of them:

Dependence on Retrieval Quality
Their performance depends heavily on the integrity of the documents retrieved during the retrieval process. If we retrieve irrelevant, obsolete, or sub-par documents, the generative model will return incorrect or false responses. This becomes especially critical if the retrieval algorithm (for example, vector search) lacks precision or the knowledge base is populated with inaccurate or biased information.

Incomplete or Missing Information
RAG systems rely on the information already in the knowledge base. If a topic is not covered exhaustively or is not up-to-date, the documents returned might not offer the contextual background needed for accurate generation. In those instances, the generative model might enclose these discrepancies with fabricated or hallucinated information.

Over-Reliance on Retrieved Context
Although it provides a better grounding, if the generative model relies too heavily on retrieved documents without reasoning about their relevance, it can trigger hallucinations. For instance, contradictory or ambiguous information in the retrieved documents could lead to logically inconsistent or incorrect responses.

Bias Propagation
If the knowledge base or retrieval mechanism is biased, the generative model can propagate these biases to the outputs. That may lead to hallucinations describing systematic issues with the data, threatening the system’s integrity.

Contextual Awareness
RAG enhances factual accuracy but may fail to address user context, leading to irrelevant or inadequate responses.

Internal Reasoning Limitations:
RAG deals with factual grounding but not intrinsically with model internal reasoning. This can result in logically inconsistent outputs.

Knowledge Graphs as a Solution

Knowledge graphs (KGs) provide a model of facts and relationships that can further improve the quality of AI systems and reduce hallucinations.

What are Knowledge Graphs?

Knowledge Graphs (KGs) are structured data representations that simulate real-world entities and the relationships between them. They structure information as nodes (individuals) and edges (relationships) to create a graph. Let’s consider a knowledge graph in a field like medicine:

The graph above illustrates the interrelations between key entities: Diabetes, Insulin, Symptoms, and Fatigue. The edges delineate the relationships, such as “Diabetes” being treated with “Insulin,” having symptoms like “Fatigue,” and being related to it, while “Symptoms” includes “Fatigue.”

This pattern allows machines to understand and analyze relationships in a human-like manner, enhancing the effectiveness of knowledge graphs for knowledge-based tasks.

Types of Knowledge Graphs

Knowledge graphs are classified based on purpose, scope, and use cases. Each is designed to address a particular need, from general knowledge representation to specific applications.

Open Knowledge Graphs: Open knowledge graphs are openly accessible and often community-built. They represent a range of general-purpose knowledge, making them versatile for natural language processing, information retrieval, and AI. For example, DBpedia draws structured content from Wikipedia, and Wikidata is a collaborative knowledge base edited by other users.
Domain-Specific Knowledge Graphs: These graphs are customized for a particular industry, like healthcare or e-commerce, offering accurate representations of domain knowledge.
Enterprise Knowledge Graphs: Enterprise Knowledge Graphs are internal business intelligence built by organizations that aggregate proprietary data from multiple sources to drive decisions, automation, and operational efficiencies. For example, Google Knowledge Graph enhances search results by linking queries with the relevant entities and relationships.
Personal Knowledge Graphs: Personal knowledge graphs focus on individual-specific data and support custom recommendations, scheduling, and task management. These graphs, standard in personal assistants and recommendation systems, personalize responses and actions according to preferences and habits. A Personal Assistant KG, for instance, could keep track of the user’s activity schedule with associations such as “User → has a meeting at → 8 AM Friday”.

Mitigating Hallucinations with RAG and Knowledge Graphs

This section covers advanced methods to minimize AI hallucinations in AI systems. We explore concepts like knowledge graph integration, advanced retrieval techniques, and dynamic knowledge graph updates.
These techniques bridge the gap between generative AI’s flexibility and the demand for reliable results.

Knowledge Graph Integration

Knowledge graph integration is an excellent approach for ensuring the accuracy of AI systems by grounding results in valid and structured knowledge. Through validation mechanisms, incoming information can be validated against a knowledge graph before being processed by the generative model.

For example, in a tool that can help diagnose diabetes, retrieved diagnostic information like “HbA1c threshold for Type 2 diabetes” can be validated with a knowledge graph containing relationships and facts from official medical literature. This process ensures that only verified information is fed into the AI system, avoiding any potential misuse of information and improving confidence in the AI system’s results.

Further, semantic constraints (defined by the knowledge graph) guide the generative model, ensuring outputs do not deviate from factual relationships. For instance, a knowledge graph could encode logic such as “HbA1c higher than 6.5% can be a sign of diabetes.” These constraints ensure the AI follows known medical facts and doesn’t risk hallucination or misrepresentation. Knowledge graph integration provides a strong framework for creating highly accurate and context-aware information.

Enhanced Retrieval Techniques

Enhanced retrieval techniques can boost the accuracy and relevance of AI systems, especially in high-risk areas such as healthcare. Advanced retrieval engines generate dense vector embeddings based on models (such as BERT or Sentence Transformers) to provide semantic matching between queries and documents.

If a clinician requests the “most recent diagnostic criteria for Type 2 diabetes,” the system can retrieve relevant guidelines and published studies that align with the intent of the inquiry, even if the exact phrasing differs. This semantic understanding allows the system to identify the most appropriate and meaningful documents, increasing the accuracy of information used for clinical decisions.

To further refine output, context-based scoring can evaluate multiple factors of relevance. A query like “HbA1c levels for diabetes diagnosis” may extract data from several sources. A good scoring system ranks documents based on their relevance to the query, source credibility (e.g., American Diabetes Association guidelines), and alignment with the query context (e.g., adult vs. pediatric patients). This scoring system ensures that the retrieved data are accurate and relevant to the particular diagnostic scenario, allowing clinicians to make confident decisions.

Feedback loops

Iterative Refinement through User Feedback
Feedback loops are essential for keeping AI systems on an improving and reliable track in complex or sensitive applications. Iterative refinement is made based on user feedback to optimize the knowledge graph’s retrieval process accuracy and completeness.
For example, in a diabetes diagnostic support system, clinicians can report irrelevant or incorrect outcomes (i.e., outdated treatment regimens or mismatched data). Such feedback could be used to update retrieval algorithms and populate the knowledge graph with more validated relationships, reducing recurrences of errors and hallucination risk for future outputs.

Incorporating human judgment into AI optimization builds trust and creates effective AI-based solutions.
A human-in-the-loop model enables continuous enhancement, evolution, and optimization of learning processes and outcomes. AI systems can learn more and improve the quality of their output by actively seeking input from users.

There are several tools to interact with and manage knowledge graphs. For example, CleanGraph is an open-source tool that allows CRUD operations on your graphs. It also allows models to be used as plugins for graph refinement and completion processes, allowing users to further improve the quality and integrity of their graph data.

Let’s consider a medical diagnosis AI system that uses a knowledge graph describing the relationship between symptoms, illnesses, and treatments. Over time, the graph can accumulate misinformation or outdated data, resulting in incorrect suggestions or hallucinations.

Using tools such as CleanGraph, medical professionals can interactively refine the knowledge graph. They can flag and rectify inaccuracies, reshape the graph to reflect new medical information, and remove outdated information. These improvements enhance the graph quality and consistency, making the AI outputs based on verified information. This reduces the risk of hallucinations and maximizes performance.

Confidence Scoring

Confidence-scoring mechanisms are necessary to make AI systems more reliable. Such processes determine the degree of retrieval certainty, determining how confident the system is in the relevance of the retrieved data.

Knowledge Graph Alignment

Confidence scoring is also essential for ensuring the alignment between extracted data and existing knowledge graphs. As an instrument of alignment, these scores can detect anomalies, ensuring that AI outputs are based on verified data. This operation is very important for the integrity of the AI systems as it minimizes the possibility of error propagation and ensures system reliability.

Let’s consider an AI system delivering medical advice. When asked what treatments are available for a given disease, the AI will respond based on the training data. To ensure accuracy and avoid hallucinations, the system allocates a confidence score to each bit of information in the response. The score describes how closely the AI-generated content correlates with a verified medical knowledge graph.

For example, if the AI suggests a treatment with extensive information in the knowledge graph, it assigns that proposal a high confidence score. On the other hand, if the AI suggests a treatment that’s not in the knowledge graph, it assigns it a lower confidence score, indicating potential misalignment.

This confidence scoring system can report low-confidence information to the system for analysis and reduce the potential of spreading false or fabricated information. This process helps improve the overall reliability of AI outputs and reduce the likelihood of hallucinations.

Fine-tuning

Fine-tuning generative models with domain-specific training improves the interpretation of structured data from knowledge graphs and minimizes misinterpretation and overgeneralization.

Hybrid models that combine generative capabilities with symbolic knowledge graph reasoning further improve factual consistency. Combined with knowledge graphs and large language models, such hybrid approaches make the best of both systems. This reduces flaws and maximizes strengths across a wide range of application domains.

Let’s consider a medical diagnosis support system made for healthcare providers. The system can provide highly accurate diagnostic recommendations by fine-tuning an LLM against a medical knowledge graph.

Implementation Steps

Integration with Knowledge Graph: Include a medical knowledge graph that contains information on diseases, symptoms, treatments, and their associations. This structured data is the factual foundation of the LLM.
Fine-Tuning the Model: Train the LLM on the included knowledge graph so it can use the relationship between medical entities. This mechanism improves the model’s performance by producing contextually consistent outputs.
Hybrid Model Development: Combine the LLM’s generative capabilities with the knowledge graph’s reasoning strengths. This combination ensures that the system suggests plausible diagnostic hypotheses and validates them against established medical knowledge.

As the LLM relates to a medical knowledge graph, the system can accurately describe the patient’s symptoms and suggest diagnoses based on verified medical facts. This alignment reduces the potential for AI hallucinations and provides clinicians with an accurate and factual diagnostic tool.

Dynamic Knowledge Graph Updates

Knowledge graphs require real-time updating to reflect recently verified information to ensure accuracy and relevance. This means that knowledge graphs-based AI systems can deliver timely, accurate results. For example, the AIR framework dynamically updates embeddings of knowledge graphs by identifying and updating triples most influenced by new knowledge events. This maintains high-quality embeddings without requiring complete retraining.
An event-based expansion model also lets knowledge graphs grow in response to events or data updates. Automated pipelines can also detect and integrate new information, updating the knowledge graph accordingly. Event-driven architectures like the ones described by Telicent allow for near real-time data distribution and computation, keeping KGs on pace with dynamic contexts and emerging knowledge.

Combining real-time updates with event-based expansion makes knowledge graphs flexible enough to learn and adapt to new information in real time. This results in AI systems producing accurate and context-sensitive outputs.

Using Existing Knowledge Graphs

Knowledge graphs are a standardized, validated approach to improve the accuracy and reliability of AI applications.
In healthcare, these graphs contextualize and validate AI output with authoritative data.
Companies can use existing knowledge graphs such as SNOMED CT and UMLS for a wealth of medical concepts, associations, and diagnostic guidelines.

A healthcare assistant, for example, can ask SNOMED CT to confirm that “HbA1c over 6.5%” is a diagnostic standard for Type 2 diabetes. The knowledge graph will maintain reliability by providing a verified link between “HbA1c > 6.5%” and “Diabetes Diagnosis” without the AI hallucinating or drawing the wrong conclusions.

Besides validating diagnostic thresholds, existing knowledge graphs are important in identifying disease risk factors.
A graph from evidence-based medicine (EBM) might show the relationships between diabetes and complications such as diabetic retinopathy, nephropathy, and foot ulcers. It can even measure these associations using metrics such as odds ratios or relative risks, allowing health professionals to analyze and manage risks effectively. Such insights are critical for designing individualized treatment or conducting population health assessments.

Using knowledge graphs with RAG models further enhances their efficiency. It retrieves relevant unstructured documents based on the user query and ensures the extracted information aligns with the knowledge graph to ensure logical coherence and factual accuracy. When searching for diabetes information, an RAG system retrieves clinical data or research papers, while the knowledge graph verifies them against set medical standards. This combination of contextual richness with the verifiability and representation of knowledge graphs produces accurate and insightful responses.

Creating Your Knowledge Graph

If a predefined knowledge graph doesn’t exist for your use case, the next step is to create a custom one that fits your area. These custom knowledge graphs are helpful in niche markets like healthcare, e-commerce, and finance, where data is specific to a company or industry.
By building a custom knowledge graph, you can convert unstructured data into a machine-readable format to be analyzed and make better decisions.

Step 1: Collecting and Preparing Data Every knowledge graph is built around quality, relevant data. First, data should be collected from trusted sources like clinical recommendations, studies, product packaging, or company databases. In healthcare, for instance, you might collect data about diagnosis, treatment, risk factors, etc., from peer-reviewed publications and guidelines such as the American Diabetes Association. To build a reliable graph, ensure the data is accurate, complete, and specific to your industry.
Step 2: Extracting Entities and Relationships
From the collected data, you can extract entities and relationships. This can be done automatically with tools such as LangChain’s LLMGraphTransformer or GPT-4. Such tools process unstructured text to identify entities (e.g., “Type 2 Diabetes,” “HbA1c > 6.5%”) and associations (“associated with,” diagnostic").
Other tools can process entities and relationships from unstructured text besides LangChain’s LLMGraphTransformer. For example, Diffbot’s Natural Language API uses text to identify entities and relationships, allowing unstructured data to be converted into structured formats.

Similarly, REBEL (Relation Extraction By End-to-end Language generation) is a BART-based sequence-to-sequence model that performs end-to-end relation extraction for over 200 different types of relations.

Step 3: Storing the Knowledge Graph
Having extracted entities and relationships, you can store the data in the graph database to query and update it efficiently. AstraDB or Neo4j are excellent choices for this purpose. These solutions allow advanced queries to gain insights or visualize the graph.
Step 4: Visualizing and Optimizing the Knowledge Graph
Visualization validates the knowledge graph and identifies patterns. You can visualize your graph using tools such as NetworkX and Matplotlib. Constantly visualizing and optimizing the graph will keep it accurate and actionable.

Developing your knowledge graph reduces hallucinations in AI systems by offering a clear and verifiable source of truth to generate responses.

Conclusion

This article discussed how RAG and knowledge graphs can help reduce AI hallucinations by building generative models on verified, structured data.
This combination approach provides better factual, semantic, and logical consistency in AI results, especially in sensitive areas such as healthcare and finance.
Using dynamic updates, advanced retrieval methods, and semantic constraints, RAG and knowledge graphs overcome the drawbacks of traditional generative models. This allows for more accurate, reliable, and trusted AI systems.