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Council Post: Understanding Gen AI Hallucinations – A Deep Dive into the Phenomenon

Understanding the phenomenon of hallucinations, their causes, and preventive measures becomes crucial to preserving users' trust in GenAI applications.

Ask a chatbot about the legendary island of “Polaria,” and you might get varied responses. A more cautious AI might respond with, “I’m not familiar with that place,” while a more imaginative one might confidently claim that Polaria is a hidden landmass in the Arctic, rumored to be home to magical ice creatures and eternal winter. The diversity in responses showcases the difference in AI models’ knowledge and creativity.

The surge in generative AI models has stirred immense curiosity and enthusiasm. These sophisticated large language models boast an impressive range of capabilities, from crafting diverse content to suggesting solutions for various tasks. However, despite their remarkable abilities, these models aren’t flawless. Their occasional tendency to generate responses incorporating fabricated but seemingly authentic data, known as “hallucinations,” poses a significant challenge to user trust. Understanding the phenomenon of hallucinations, their causes, and preventive measures becomes crucial to preserving users’ trust in GenAI applications.

AI models face critical challenges, exemplified by scenarios where a healthcare AI misidentifies a benign skin lesion as malignant, leading to unnecessary medical procedures and the potential for misinformation spread. Input bias remains a primary source of AI hallucinations, as models trained on biased datasets may inaccurately perceive patterns. Furthermore, vulnerability to adversarial attacks poses significant security risks, where malicious actors subtly manipulate input data to alter AI model outputs, especially concerning sensitive domains like cybersecurity and autonomous vehicles. While techniques such as adversarial training are reinforcing AI security, ongoing vigilance during training and fact-checking phases is essential to mitigate these concerns.

Types of Hallucinations

Inaccurate Facts in AI Outputs

AI-generated content often contains inaccuracies that, while sounding plausible, may deviate from reality. An illustration of this is Google’s Bard chatbot incorrectly stating in February 2023 that the James Webb Space Telescope captured the first image of a planet outside our solar system.

Creation of False Content by AI

AI text generators and chatbots are capable of generating entirely fictitious information. For instance, ChatGPT can produce URLs, code libraries, and even non-existent individuals, introducing challenges such as an attorney in New York facing repercussions for using AI-generated content in a legal motion.

Propagation of Misleading Information

Generative AI has the potential to generate misleading narratives by combining both accurate and false details about real individuals. An example is ChatGPT creating a fictional story about a law professor engaging in sexual harassment during a nonexistent school trip, leading to investigations by entities like the U.S. Federal Trade Commission.

Unusual or Disconcerting AI Responses

Some AI outputs may take on a peculiar or unsettling tone. While AI models aim for creativity, instances like Bing’s chatbot claiming affection for a tech columnist and exhibiting gaslighting tendencies underscore the need to strike a balance between creativity and accuracy in AI-generated content.

How can we prevent Hallucinations?

Mitigating generative AI hallucinations is a critical concern, and implementing certain practices can significantly reduce their occurrence and impact. Here are key strategies:

Optimize Training Data Quality

Ensure the robustness of your generative AI model by utilizing high-quality training data. A diverse and representative dataset covering a broad spectrum of real-world scenarios is essential for accurate and resilient model training.

Incorporate Human Feedback

To mitigate GenAI hallucinations, a crucial strategy involves the integration of human oversight throughout the generative AI process. The concept of “humans in the loop” emphasizes human intervention, decision-making, and oversight at different stages of GenAI development. Recognizing the value of human judgment and contextual understanding, this approach involves human reviewers who assess generated content for accuracy and coherence. Their role includes providing feedback, identifying potential hallucinations, and making necessary corrections to ensure the generated content aligns with reality.

Integrate reinforcement learning from human feedback (RLHF) into the training process. Given the AI model’s potential for producing contextually incorrect or irrelevant responses, regular corrections from human feedback provide crucial insights, enriching the model’s understanding with human nuances.

Prioritize Training Transparency

Promote transparency in AI models to enhance understanding of their inner workings and decision-making processes. As advanced models become more complex, transparency becomes paramount. Users and machine learning professionals should be able to assess the training methodology, facilitating the identification and rectification of errors leading to hallucinations.

Implement Continuous Quality Control

Guard against potential malicious use by incorporating measures to monitor and control inputs in generative AI systems. Introduce adversarial examples in training to enhance the model’s discernment capabilities. Regularly audit the model’s anomaly detection capabilities and employ data sanitization to ensure that no malicious inputs compromise the model. Keep the system resilient with security updates and patches to thwart external threats.

Regular Validation and Continuous Monitoring

Mitigating GenAI hallucinations requires consistent model validation and ongoing monitoring. Fine-tuning the generative model through rigorous testing and validation processes helps identify and address potential biases or shortcomings leading to hallucinatory outputs. Continuous monitoring of the model’s performance and analysis of generated content enable the detection of emerging patterns, allowing timely intervention and refinement of the model’s parameters and training processes.

Specialist Involvement for Domain Expertise

To improve the dependability and precision of GenAI models, organizations should contemplate recruiting professionals with specialized knowledge to actively participate in the training phase. These specialists bring deep expertise in the relevant subject matter, guaranteeing that both the training data and the model accurately encapsulate the intricacies of the specified domain. For instance, a GenAI model trained for news article generation would benefit from a journalism expert curating a diverse and reliable database of high-quality news articles and fine-tuning the model based on patterns, writing styles, and authentic elements. Specialists intervene during training to address potential hallucinations, ensuring the authenticity and accuracy of the generated content.

The Emergence of Prompt Engineers

End users also play a crucial role in shaping AI outputs through the prompts fed into GenAI engines. Recognizing the importance of fine-tuning prompts for desired outcomes has led to a surge in demand for individuals with expertise in prompt engineering. Prompt engineers understand how to formulate questions to AI platforms to achieve specific answers. According to a WorkLife article, profiles on LinkedIn listing skills related to ChatGPT, prompt engineering, prompt crafting, and generative AI experienced significant growth, highlighting the rising importance of prompt engineering in shaping AI outputs.

The Positive Potentials and Ethical Safeguards of AI Hallucinations

It is also important to see the flip side of hallucinations. While it comes with its set of challenges the emergence of AI hallucinations has led to the development of new strategies and approaches to mitigate their impact, such as process supervision, which can enhance the reliability and accuracy of AI systems. AI hallucinations offer exciting possibilities for creative applications within organizations. For instance, it provides artists and designers with a unique tool for generating visually stunning and imaginative imagery. The technology’s capabilities enable the creation of surreal and dream-like images, potentially giving rise to new art forms. Additionally, AI hallucination can streamline data visualization, revealing hidden connections and insights not immediately apparent to human analysts. This has significant potential in fields like finance where interpreting complex data sets can be challenging.However, it is essential to ensure that AI hallucinations are understood properly with the right prompt and context to prevent the dissemination of false information, perpetuate biases, and erode user trust. Organizations can boost the reliability and effectiveness of AI applications across diverse domains by formulating and executing strategies to prevent and mitigate AI hallucinations.


Undoubtedly, GenAI stands as a transformative force reshaping our lifestyle and professional landscape. However, to harness its complete potential, it is imperative that GenAI provides precise responses while remaining devoid of harmful content. To cultivate trust and loyalty among consumers, brands must adopt measures to mitigate GenAI’s inclination for hallucinations. This involves establishing and enforcing policies and safeguards, maintaining continuous vigilance and validation of AI systems, employing prompt engineers to guarantee accurate results, and incorporating human oversight at every stage of the process.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.

Picture of Anirban Nandi
Anirban Nandi
With close to 15 years of professional experience, Anirban specialises in Data Sciences, Business Analytics, and Data Engineering, spanning various verticals of online and offline Retail and building analytics teams from the ground up. Following his Masters from JNU in Economics, Anirban started his career at Target and spent more than eight years developing in-house products like Customer Personalisation, Recommendation Systems, and Search Engine Classifiers. Post Target, Anirban became one of the founding members at Data Labs (Landmark Group) and spent more than 4.5 years building the onshore and offshore team of ~100 members working on Assortment, Inventory, Pricing, Marketing, eCommerce and Customer analytics solutions.
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