Artificial Intelligence is no longer a novelty. It’s fundamental to how modern products serve customers.
Whether it’s a chatbot guiding support queries, a dashboard surfacing insights, or a recommendation engine tailoring offers, AI is now at the heart of the user experience. To lead in this space, product leaders must understand two key behaviours of AI performance: inference and hallucination.
These aren’t just technical terms, they’re fundamental concepts that shape user trust, product accuracy, and business outcomes.
What Is Inference?
Inference is the process by which an AI model uses input data to produce a result. This is based on patterns learned during training. It’s how AI applies what it knows to new situations.
Think of inference as a well-trained assistant in a vast archive. Ask them a question, and they’ll scan what they’ve already read to offer a precise, relevant answer. That’s inference: grounded, informed, and helpful.
For instance, a product recommendation engine might notice that a user often buys outdoor gear, so it suggests hiking boots during a winter sale. That’s successful inference. It’s personalised, timely, and based on prior user behaviour.
What Is Hallucination?
Hallucination is when an AI system generates information that sounds plausible but is false, misleading, or entirely fabricated. This is common in large language models, which are designed to sound fluent. They sound confident and eloquent even when they’re totally wrong.
Think of our assistant in the archive. This time, instead of finding a relevant resource, they invent a book that doesn’t exist. They provide you with a book summary and Harvard-style reference, and send you off to find it. You leave impressed. Until you realise you’ve been misled.
For example, a healthcare chatbot might respond to a medical question with confident, scientific-sounding advice that turns out to be inaccurate. This could be dangerous.
Why It Matters for Product Leaders
Understanding the difference between inference and hallucination isn’t just an academic question. It’s central to product design and user experience. As AI becomes embedded in everyday workflows, the consequences of getting it wrong grow more serious. Product leaders must proactively manage these behaviours, not react to them.
Here are three considerations that highlight why this understanding is so important.
1. Trust Drives Retention
Users expect speed and fluency from digital products, but they also need to trust you. A hallucinated response, especially in a sensitive context, can instantly destroy the trust that took years to build.
A user who receives a misleading answer from an AI assistant may escalate to human support. In the short run, this makes your product more costly to operate. Ultimately, if this happens enough times, your product stops being useful. It could even become a liability that strangles your credibility.
2. Accuracy Isn’t Optional
AI generated results must be accurate. The more confidently it gets things wrong, the less users will believe it when it gets things right.
In regulated industries like finance, health, and legal services, hallucinations aren’t just inconvenient mistakes, they’re unacceptable risks. A fabricated investment result or medical stat can lead to compliance issues, lawsuits, financial penalties, and public backlash.
3. AI Isn’t Magic
It’s tempting to treat AI as a black box that simply works. While it is harmless to have this impression as a casual user of ChatGPT, product leaders must have a clearer understanding of what AI can and can’t do. That means setting clear internal expectations and helping stakeholders, from executives to engineers, grasp its limits.
Real-World Impacts
When your AI system infers well, the effects are immediate and valuable. Users receive faster, more relevant responses. Product interactions feel fluid and intelligent. Satisfaction rises, engagement deepens, and trust compounds with each positive experience.
But when AI hallucinates, even once in a critical moment, the cost can be severe. Confidence falters, support tickets spike. What began as a frictionless user journey devolves into frustration, confusion, and churn.
And the consequences don’t stop at the UX layer.
Hallucinations carry financial and reputational costs. Users who abandon your product after acting on flawed recommendations represent lost revenue. When they tell their friends not to buy your product, the effect is compounded.
In regulated sectors, inaccurate outputs can also expose your business to legal risk. Over time, occasional hallucinations can erode the one thing your product depends on, trust.
What You Can Do
Hallucinations aren’t just a technical flaw, they’re a product risk. But they can be managed.
Product leaders don’t need to become machine learning experts, but they do need a practical toolkit for reducing AI’s failure modes.
Here are four steps that can make a real difference.
1. Choose the Right Model
Not all AI models are built for accuracy. If your product requires fact over flair, prioritise models designed for accuracy. For example, Harvey, an AI platform developed for law firms and professional service providers, is fine-tuned for precision and reliability. Firms like Ashurst, Allen & Overy, and PwC have adopted Harvey to solve problems more quickly with reduced risk of hallucinations in contract analysis and legal research.
2. Use Safety Nets
Even the most advanced AI models can hallucinate. To prevent false information reaching end users, product leaders should layer in safety mechanisms that catch or correct errors before they impact customers.
- Retrieval-Augmented Generation (RAG) can enhance a model’s accuracy by grounding responses in a trusted knowledge source. Rather than relying on the model’s training data, RAG systems inject known facts into the response. For example, Perplexity AI , a web search engine, uses a RAG system and improves trustworthiness of responses by including citations.
- Human-in-the-loop (HITL) is an approach that augments rather than replaces humans. Experts provide oversight by checking, editing, and approving AI output before it goes live. This approach is critical in high-stakes environments like law, finance, medicine, and management consulting. For example, McKinsey has emphasised the importance of human oversight, especially when using generative AI to develop actionable recommendations for clients.
- Fact-checking APIs integrate third-party verification tools that automatically scan generated content for inaccuracies. These tools compare responses to authoritative sources and flag potential errors. For example, Google’s Fact Check Tools API has been used in journalism to verify claims in real time, reducing the chance of spreading misinformation before content is published.
3. Track Hallucination Rates
Monitor hallucination like you would downtime or churn. It’s a performance metric. Track it, understand it, and take steps to reduce it. For example, OpenAI has developed a benchmarking tool called SimpleQA that assesses the accuracy of its language models. The firm has revealed that GPT 4.5 has a hallucination rate of around 37%.
4. Be Transparent
Set realistic user expectations. Let people know when a response is AI-generated, and when it might be wrong. Users value honesty more than perfection. For example, GitHub Copilot adds code references to increase transparency and trust by showing developers when AI-generated code suggestions match existing public code.
The bottom line
AI is a powerful tool. But just like a loaded military rifle, it’s dangerous if you don’t understand it and use it properly.
Inference makes AI useful. Hallucination, left unchecked, makes it dangerous. Product leaders must design around both.
The firms that win with AI won’t be those that deploy it everywhere. It’ll be those who understand how it should be used and when it should be trusted.
Don’t just build with AI. Build systems you can explain, monitor, and trust. AI will soon be everywhere, but trust remains a scarce resource. It’s what great products, and enduring companies, are built on.
Zuhair Imaduddin is a Senior Product Manager at Wells Fargo. He previously worked at JPMorgan Chase and graduated from Cornell University.
Image: DALL-E
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