What do you need to know about AI hallucination?

Here’s what many AI vendors won’t tell you: making things up isn’t a bug in large language models—it’s their default behaviour. These systems were trained to be excellent writers, not fact-checkers. They learned from millions of documents containing everything from encyclopedia entries to fantasy novels, optimising for one thing: sounding convincing.

Think of it this way: these models are like incredibly talented improvisers who never break character. When they don’t know something, they don’t pause or admit uncertainty—they keep the performance going.

How has the AI industry tried to reduce hallucination?

The Instruction Approach

Modern AI systems undergo special training using Q&A datasets and factual information. While this helps, it’s like teaching that talented improviser to be more careful—they’re still fundamentally wired to fill gaps with plausible-sounding content.

The Verification Challenge

Recent analysis¹ identifies two core problems:

• Limited capability: Current models can’t reliably distinguish between what they actually know and what they’re making up

• Competing priorities: Training AI to be helpful often conflicts with training it to be accurate—especially when the system is rewarded for attempting answers rather than acknowledging limitations

The “Reasoning Model” Paradox

The latest generation of AI models, trained to excel at coding and mathematics, illustrates this perfectly. These systems are rewarded only for getting the right answer—there’s no partial credit. Result? When faced with problems beyond their capability, they generate pages of sophisticated-looking nonsense rather than simply saying “I don’t know.”

What is the hidden risk in your AI strategy?

Imagine asking your AI assistant for last quarter’s revenue figures, and it confidently responds with numbers that sound plausible but are completely fabricated. This isn’t a hypothetical scenario—it’s a daily reality with current AI systems. Understanding why this happens is the first step toward deploying AI you can actually trust.

What does “hallucination-free AI” mean for regulated industries?

The implications are significant:

• Financial Services AI: AI that can analyse market data without inventing trends

• Healthcare AI: Systems that distinguish between evidence-based recommendations and speculation

• Legal AI: Document analysis that flags uncertainties rather than filling gaps with assumptions

• Customer Service AI: Chatbots that know when to escalate rather than improvise answers

Will AI be reliable in the future?

The AI industry is at a crossroads. We can continue hoping that larger models with more training will eventually stop hallucinating, or we can build systems architected for trustworthiness from day one.

While the broader industry works on improving neural models’ world understanding and uncertainty estimation—progress that will likely take years—businesses need reliable AI solutions now. That’s why we believe the future lies in combined approaches that don’t compromise accuracy for fluency.

At UnlikelyAI, we’re not waiting for perfect neural networks. We’re building the reliable AI systems that businesses need today, using neurosymbolic techniques that ensure every output can be traced, verified, and trusted.

Because in the real world, “sounds plausible” isn’t good enough.

How can you fix AI hallucinations: Neurosymbolic AI

At UnlikelyAI, we’ve stepped back to ask a fundamental question: Why are we trying to force pattern-matching systems to be truth engines?

Our neurosymbolic approach combines the communication abilities of neural networks with the logical rigour of symbolic reasoning. Think of it as pairing a brilliant speechwriter with a meticulous fact-checker who reviews every claim before it’s spoken.

This isn’t about making incremental improvements to existing technology—it’s about building AI systems designed from the ground up for reliability.

¹ Source: “Why Do LLMs Hallucinate” - LessWrong. https://www.lesswrong.com/posts/pK9W3ttsBDDu2nojX/why-do-llms-hallucinate