The Truth Behind AI Hallucinations: Why Generative Models Fabricate Facts and How to Tame Them

Generative AI’s ability to effortlessly create human-like text, images, and even audio has enhanced efficiencies and capabilities never imagined before. However, along with exciting new capabilities, training AI is faced with plenty of challenges. Further, there is a fundamental weakness: generative models are notorious for creating deceptive yet highly plausible information, called "AI hallucinations." 

It's important to understand why these hallucinations occur if you’re an AI developer. However, the scope expands to businesses, policymakers, and everyday users who rely on AI-generated output. What influences an AI model to "lie?" And how can you better understand and address it as you weave generative AI further into your processes and practices? 

Let’s first understand what are AI hallucinations. 

What Are AI Hallucinations? 

Let’s begin with a real example. Our marketer recently used ChatGPT to look up a list of industry events. The prompt included the year and month as well. Yet, while AI returned almost immediately with what appeared to be an impressive list, most of the events were from 2024. 

So, a question for you: Regardless of how well you understand prompt engineering, when you use generative AI, do you roll with the information generated? Or do you fact-check? 

AI hallucination describes confident yet factually incorrect output from generative models that appear plausible. While human lying is typically motivated by intent, hallucinations are artifacts of probabilistic modeling - it reflects the way the system is developed, trained and prompted. 

Examples of Real World

• Fabricated academic citations - The models are able to generate scholarly-looking references with unverifiable citations. 

• Imaginary case studies or news events - The model provides rich detail backstories or usage statistics that are fictitious. 

• Unsound medical or legal advice - especially harmful for high-stakes environments. 

• Irrational or physically impossible visual content: In image-generation AIs, hallucination can take the form of visual compositions that are illogical or anatomically impossible.  

These problems exist even while output tends to become increasingly sophisticated in terms of grammar and sounding authoritative, making detection difficult for all but the savviest. 

As we go further, let’s understand what causes AI hallucinations. 

Why do generative AIs hallucinate? 

The causes of hallucination are both technical and systemic, and knowing the causes is the first step to preventative action. Here are 5 causes for AI Hallucinations: 

1. Limitations and biases in training data 

• Data gaps: Each training dataset lacks certain specifics about certain facts, contexts, or languages; when this happens, the AI makes improvised choices.  

• Bias replication: AI models can replicate inaccuracies, stereotypes, or outright errors present in the underlying data.  

• Domain blindness: The vast majority of models are not specialists; when prompted to operate outside their area of specialty, the error rate increases.  

2. Model architecture and overfitting 

• Overfitting: When a model memorizes quirks of its dataset rather than underlying truths, it may misapply knowledge to unfamiliar inputs. 

 

• Creative completion: In generative models, the entire underlying mechanics are based on predictions of 'what generates next' statistically. AI output is not based on objective verification, favoring fluency over fidelity.  

 

3. The prompt and contextual constraints  

• Ambiguity: Indirect, vague, or poorly-scoped prompts can motivate models to guess or generate incorrect details.  

• Prompt length: Longer or multi-part prompts can increase the probability of hallucination, especially if the model must maintain complex reasoning capabilities without grounding.  

4. Limitations of Real-Time Retrieval 

Some of the models combine pre-trained knowledge with information from databases or the web. This limitation, caused by retrieval failures or the use of less-than-authoritative sources, may insert new inaccuracies into outputs.  

5. Human and AI feedback loops 

If incorrect or misleading output is not caught and corrected in reinforcement learning, it may train the model to repeat those errors. 

Now that we know what causes AI hallucinations, let’s dive deeper. How does it impact users? 

The Threat to Trust, Safety, and Adoption  

1. Loss of User Trust 

Recurrent hallucinations sow distrust into AI produced content, and this may limit adoption and innovation.  

2. Disseminating false information

Once information (text, images, or videos) produced by the AI goes viral, errors spread quickly on the internet and to reputable publications, academic work, and decision pipelines.  

3. Professional and Legal Exposure 

If a hallucinated output reverberates in law, medicine, and finance, there may be regulatory or life-and-death consequences. Companies are increasingly liable towards their clients when it comes to "AI lies".  

4. Operational Failures 

Relying excessively on generative AI for critical business activities, or in time sensitive situations (like writing software, reviewing contracts, or other medical reports), can introduce subtle, expensive, and hard-to-detect errors. 

Are Hallucinations Deteriorating Over Time? 

The extent and amount of hallucination have improved due to innovations in model architectures, safety layers, and retrieval-based methods in numerous recent versions of models. Enhanced models featuring greater capabilities and creativity can hallucinate in new ways and means when dealing with open-ended or complex areas.  

• Routine task: On simple prompts, hallucination rates have improved to reach at least lower than 3%. 

• Complex queries: In open-ended generation or function executing with novel step-by-step reasoning, hallucination still introduces a substantial risk that triggers higher hallucination rates and unpredictability in outputs. 

No model is currently immune, so vigilance and supervision are required. But how do we do that? 

Managing and Mitigating AI Hallucinations 

Phase of a holistic, end-to-end approach that integrates technical strategies, human supervision/safeguards and organizational policies.  

1. Diverse, Curated, and High-Quality Training Data 

• Curation: schedule regular audits, cleaning and expansion of your training datasets, to ensure an appropriate level of coverage, accuracy, and representation.  

• Domain-specific supplementation: If your industry has a high risk of hallucination, utilize direct expert-labeled ground truth data with regular testing in real-life scenarios.  

2. Reinforcement Learning with Human Feedback (RLHF)  

• Expert review: Design to allow human-in-the-loop validations, especially for outputs that are highly specialized and or sensitive.  

• Continuous feedback loops: Motivate users and reviewers to flag hallucinations, then feed these corrections back through as training data into the model. 

3. Retrieval-Augmented Generation (RAG) 

• Fact-checking at generation: Hybrid models combine generation with trusted sources or internal databases, potentially reducing fabrication. 

• Citation of evidence: Require or encourage models to cite supporting information to allow traceability and review. 

4. Prompt Engineering and User Education 

• Clear instructions: Design prompts communicating the context, scope, and reference requirement. 

• Out-of-domain prompts: Build safeguards to signal when prompts are outside of AI domain expertise, either by warning or escalating the prompt to human review. 

5. Model Improvements and Transparency 

• Confidence scoring: Build AI systems that can identify uncertainty or the likelihood of hallucination in their answers. 

• Transparency: Document limitations to knowledge, hallucination considerations, and changes across model releases. 

6. Automated Detection Tools 

• Internal checks: Have the AI use algorithms to find internal inconsistencies, citations to nonexistent references, or low-likelihood outputs. 

• Third-party evaluation: Have models independently audited regularity to look at the hallucination rate and fallout. 

 

7. Layering Supervision in Workflows 

• Human review in high-stakes tasks: If outputs will be subject to compliance or a high-stake system, indicate review needed before human dissemination. 

• Automated and manual hybrid: It is possible to integrate a layer of automated checks with trained human judgment for the best outcome. 

Hallucination Management in Practice: A Scenario-based Perspective 

Scenario 1: Healthcare documentation 

Risk: A generative model states an incorrect drug interaction or incorrect dosage.  

Best Practice: All outputs should have an expert review, citation of source data in the output, and RLHF with constant input from practicing medical professionals.  

Scenario 2: Academic research summaries  

Risk: AI invents studies, misstates conclusions, or conflates unrelated findings.  

Best Practice: Outputs indicate new or unrecognized references for verification; push for postprocessing by subject matter experts.  

 

Scenario 3: Customer support automation 

Risk: AI creates company policies or product features.  

Best Practice: Answers are limited to pre-approved knowledge bases; ambiguous or new requests escalate to a human agent.  

Beyond Hallucinations: Building Responsible and Trustworthy AI 

• Continuous education: Keep users aware of the limitations of AI - fact check and skeptically scrutinize, regardless of how "natural" or authoritative the answer sounds.  

• Governance frameworks: Follow industry standards on AI risk, reliability, transparency.  

• Cultural shift: See AI as a powerful collaborator, not as a perfect oracle. Value human judgment as the final authority in all important decisions. 

Hallucinations are not the result of flawed AI—the reality is that they are the result of creative, data-fueled systems functioning at the boundaries of language and knowledge. 

By identifying from where "AI lies" arise, organizations and users can apply a variety of solutions to reduce or limit hallucinations. 

Trustworthy AI will involve technology, proper planning and design, and thus a large organization commitment to transparent governance. 

In order to make progress on hallucinations - whether it be through the approach of technology, process, or a shared accountability - organizations/developers and users alike, may begin to capitalize on the promise and potential of generative models while still assuring accuracy, credibility and trust from humans. 

Quick-Reference Table: AI Hallucination Causes and Solutions 

HALLUCINATION CAUSE	MANAGEMENT STRATEGY
Gaps or bias in training data	Data curation, expert augmentation
Overfitting/misapplied knowledge	Model refinement, robust validation
Ambiguous or open-ended prompts	Prompt engineering, guardrails, warning systems
Lack of grounding/source retrieval	RAG frameworks, enforcement of evidence citation
Missing or inadequate user feedback	RLHF, user education, feedback channels
Overreliance on AI for critical tasks	Mandatory human review, workflow integration

You have reached the end. Thank you for reading our blog. We hope you found it informative and useful. For more such content on to help you stay informed on AI and our language services, you can check out our blog page here.

If you have any feedback or suggestions on what you’d like for us to cover or how we can make our blogs more useful, you can reach us through our LinkedIn inbox or email us at digital@crystalhues.in.