How to handle AI hallucinations in regulated work

For many UK small and medium businesses, the prospect of integrating AI tools like Microsoft Copilot into daily operations is exciting. Improved efficiency, faster insights, and enhanced productivity are often the headline benefits. However, as with any powerful technology, there are nuances and potential pitfalls that require careful consideration. One of the most frequently discussed and misunderstood issues is AI hallucination. In simple terms, this is when an AI generates information that sounds plausible and authoritative but is, in fact, incorrect, nonsensical, or entirely fabricated.

While a minor factual error in a marketing draft might be easily corrected, the stakes are significantly higher in regulated industries such as finance, legal, healthcare, or construction. Here, inaccurate information can lead to regulatory breaches, financial penalties, reputational damage, or even legal action. This article will explore how UK SMBs operating in regulated environments can proactively address and mitigate the risks associated with AI hallucinations.

Understanding the 'Why' Behind Hallucinations

Before we can manage hallucinations, it helps to understand why they occur. Large Language Models (LLMs) like those powering Copilot are trained on vast datasets of text and code. They learn to predict the next word in a sequence based on patterns they have observed. They are excellent at identifying relationships and generating human-like text, but they do not "understand" information in the way a human does. They lack true knowledge, intent, and common sense.

When an LLM hallucinates, it's often due to one or more of these factors:

• **Insufficient or Ambiguous Training Data:** If the training data contains biases, errors, or doesn't cover a topic thoroughly, the model might fill in gaps with plausible but incorrect information.
• **Complex or Vague Prompts:** Asking an AI a highly ambiguous or overly complex question can lead it to "guess" or infer information that isn't supported by its knowledge base.
• **Confabulation:** The model might combine disparate pieces of information from its training data in a way that creates a novel, but false, 'fact'.
• **Overgeneralisation:** Applying patterns learned from one context too broadly to another where they don't apply.
• **Out-of-date Information:** While some models are regularly updated, there can still be a lag, meaning they might present information that was once correct but is now outdated.

Recognising these underlying causes is the first step in developing robust mitigation strategies.

Implementing Robust Human Oversight and Verification

The most critical defence against AI hallucination in regulated work is, and will remain, human oversight. AI tools are assistants, not replacements for human expertise and accountability.

• **Mandatory Review Workflows:** Establish clear policies that dictate every piece of AI-generated content or analysis, especially concerning regulated activities, must undergo a thorough human review. This isn't optional; it's a non-negotiable step.
• **Expert Verification:** Ensure reviewers are subject matter experts. They must possess the necessary knowledge to identify inaccuracies, confirm compliance with regulations, and understand the context in which the AI output will be used.
• **Clear Ownership and Accountability:** Define who is ultimately responsible for the accuracy and compliance of the final output. The AI does not bear this responsibility; your human employees do.
• **Source Checking:** Train staff to cross-reference any claims, figures, or statements generated by AI with official or authoritative sources. For Copilot, this might involve verifying its generated summaries against the original documents it references.

Treat AI-generated content as a first draft, a hypothesis, or an input, never as a definitive final product for regulated contexts without stringent human validation.

Developing Smart Prompt Engineering Strategies

The quality of AI output is heavily influenced by the quality of the input it receives. Poorly constructed prompts can significantly increase the likelihood of hallucinations.

• **Be Specific and Clear:** Avoid vague language. Clearly define the task, the desired output format, and any constraints or requirements. For example, instead of "Summarise this document," try "Summarise section 3.2 of this document, focusing on the financial implications, and provide three bullet points highlighting key risks, citing specific paragraph numbers for each risk."
• **Provide Context:** Give the AI as much relevant background information as possible within the prompt or by selecting relevant files for Copilot to reference. The more context it has, the less likely it is to invent details.
• **Request Sources:** Where possible, instruct the AI to cite its sources. For Copilot, this is often integrated, but reminding it to only use information from the provided documents can be helpful. "Only use information from `Document A.docx` and `Report B.pdf`."
• **Iterative Prompting:** If the initial output is unsatisfactory, refine your prompt. Break down complex requests into smaller, more manageable steps. Ask follow-up questions to clarify or expand on specific points.
• **Boundary Setting:** Explicitly state what the AI should *not* do. "Do not speculate on future market conditions" or "Do not provide legal advice, only summarise the facts presented."

Effective prompt engineering is a skill that needs to be developed and refined within your organisation. Investing in training your staff on this will pay dividends in reducing AI errors.

Establishing an AI Governance Framework

Beyond individual usage, your SMB needs a structured approach to AI deployment, especially in regulated areas.

• **AI Usage Policy:** Develop a clear internal policy outlining acceptable and unacceptable uses of AI, particularly concerning sensitive data and regulated tasks. Specify the level of human review required for different types of AI output.
• **Risk Assessments:** Conduct regular risk assessments for each AI tool deployed, specifically evaluating the potential for hallucination and its impact on your regulatory obligations.
• **Documentation and Audit Trails:** Maintain records of AI use, including prompts, outputs, and any human modifications or verifications. This creates an audit trail that can be invaluable in case of an incident or regulatory inquiry.
• **Employee Training:** Provide ongoing training to all employees using AI tools. This should cover not only how to use the tools effectively but also the limitations of AI, the importance of verification, and the organisation's specific AI policies.
• **Feedback Mechanisms:** Establish ways for employees to report instances of AI hallucination or unexpected behaviour. This intelligence can help refine policies, improve prompt strategies, and inform decisions about AI tool capabilities.

A robust governance framework transforms ad-hoc AI use into a controlled and compliant process.

Continuous Monitoring and Adaptation

The AI landscape is constantly evolving. What works today might need adjustment tomorrow.

• **Stay Informed:** Keep abreast of updates from AI providers (like Microsoft for Copilot) regarding new features, improved capabilities, and known limitations.
• **Regular Policy Review:** Your AI governance policies should not be static. Review and update them regularly to reflect changes in technology, regulations, and your organisation's operational needs.
• **Performance Metrics:** While challenging, consider ways to track the frequency and impact of AI hallucinations within your specific workflows. This data can inform training needs or decisions about where AI is best applied.
• **Share Best Practices:** Encourage employees to share effective prompt strategies and verification techniques. Foster a culture of learning and continuous improvement around AI use.

Addressing AI hallucinations in regulated work is not about eliminating them entirely-which is currently impossible-but about building robust processes and fostering a disciplined approach so that their impact is effectively neutralised before it causes harm. By combining thorough human oversight with strategic prompt engineering, a comprehensive governance framework, and a commitment to continuous learning, UK SMBs can confidently leverage AI tools while maintaining regulatory compliance and protecting their business.

Your Next Steps

Begin by auditing your current or planned AI use cases, especially those touching regulatory compliance. Identify where AI outputs would require the highest level of scrutiny. Then, schedule an internal workshop with key stakeholders and subject matter experts to start drafting your AI usage policy and reviewing your prompt engineering practices. This proactive approach will help ensure you are prepared to manage AI effectively and responsibly.