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AI Hallucination Is Real. So what can you do about it?
- The AISI Team
- Nov 6
- 3 min read
Artificial Intelligence (AI) tools like ChatGPT, Claude, and other generative AI systems are becoming essential in workplaces across Singapore. While these tools offer powerful automation and efficiency gains, they also come with a critical weakness: AI hallucinations—outputs that are factually wrong, misleading, or entirely fabricated.
For businesses, hallucinations are not harmless mistakes. They can cause legal issues, financial losses, reputational damage, and incorrect decision-making. This article explores high-profile examples of AI hallucinations, why they occur, and modern techniques such as RAG and AI pipelines that reduce or eliminate them.
What Are AI Hallucinations?
AI hallucinations occur when large language models (LLMs) generate incorrect, fabricated, or unsupported information with complete confidence. This happens because LLMs predict likely word sequences; they do not inherently validate facts.
High-Profile Real-World Cases of AI Hallucination
Case 1: Lawyer Submits Fake AI-Generated Citations
In 2023, a U.S. lawyer used ChatGPT to prepare a legal filing. The AI generated six fabricated court cases, complete with fake citations and fictional legal arguments.
The lawyer was fined, and the incident gained global media attention.
Case 2: Google Bard’s Wrong Answer Causes $100 Billion Market Drop
During Google’s live launch of its AI chatbot Bard, the model gave an incorrect fact about the James Webb Space Telescope.
This error caused Alphabet’s stock to drop by 9%, erasing over $100 billion in market value.
Case 3: Meta’s Galactica AI Fabricates Research Papers
Meta released Galactica as an AI system to help scientists generate academic content. Within two days, researchers exposed the model producing fabricated scientific references and nonsensical papers.
Meta removed the system following widespread criticism.
Case 4: Stack Overflow Bans ChatGPT Answers
Stack Overflow temporarily banned AI-generated answers in 2022 due to “high volumes of answers with low accuracy,” overwhelming moderators and risking misinformation.
Why AI Hallucinations Are Dangerous for Businesses
- Legal exposure from incorrect statements
- Faulty financial summaries or analytical reports
- Compliance breaches
- Serious reputational damage
- Incorrect SOPs or training materials
- Incorrect technical or coding outputs that introduce vulnerabilities
- Loss of trust among customers and staff
How to Reduce or Prevent AI Hallucinations
Solution 1: Retrieval-Augmented Generation (RAG)
RAG ensures the AI only answers using your own verified documents and knowledge base. It retrieves real data first, then uses the LLM to generate an answer strictly based on that information.
How it works:
Users ask a question
The system searches your internal documents/databases
Only relevant chunks are fed to the AI
AI generates an answer based strictly on retrieved facts
Benefits:
Greatly reduces hallucinations
Always up-to-date
Ensures compliance
Prevents fabricated citations
Customised for your organisation’s knowledge
No data leaves your secure environment
Solution 2: Building AI Pipelines
Instead of giving AI full freedom, building company-specific pipelines break tasks into stages:
Example:
Interpret question
Retrieve data
Validate data
Draft response
Apply safety rules
Final output
Pipelines:
Add validation layers
Prevent unsupported answers
Enforce business rules
Reduce errors and hallucinations
Think of it as “AI with a safety supervisor.”
Solution 3: Use Enterprise or Private AI Models
Private deployments ensure:
- Full data isolation
- Logging and audit control
- Safe internal usage
- Compliance with PDPA and organisational policies
Solution 4: Fine-Tuning with Internal Data
Use documents or data that are specific to your company as the data to train your LLM. LLMs trained on:
Your SOPs
Your product manuals
Your compliance rules
Your internal documents
Models trained on your internal data produce:
- More accurate
- Industry-aligned
- Safer
- Less generic answers
Solution 5: Implement Governance, Monitoring & Safety Guardrails
Effective organisations set clear guidelines:
- AI use policies
- Data handling rules
- Allowed vs. restricted tasks
- Regular accuracy evaluations
- Monitoring for misuse
AI is not always right. Be aware of the risks and take steps to prevent hallucination.
AI hallucinations are not a minor flaw—they are a business risk. Without guardrails, hallucinations can cause real harm: incorrect reports, legal issues, reputational damage, and operational mistakes.
However, with the right setup—RAG, AI pipelines, private deployments, fine-tuning, and governance—businesses can use AI safely and reliably. AI becomes a strategic advantage, not a liability.
Using AI in your work but want to avoid AI hallucination? Let's chat.
Sources & References:
New York Times – “Here’s What Happens When Your Lawyer Uses ChatGPT”:
Reuters – “Google’s Bard chatbot shares inaccurate information”:
MIT Technology Review – “Meta’s new AI model is dangerous”:
The Verge – “Stack Overflow bans ChatGPT answers”:
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