Detecting AI Hallucinations Is Harder—And More Important—Than Ever

Early AI mistakes were easy to laugh off. Today’s hallucinations are harder to spot, easier to spread, and more dangerous. Subtle AI errors matter—and better design can prevent them.

Remember when ChatGPT confidently told us there are only two R’s in strawberry? Or that Egypt had been transported across the Golden Gate Bridge not once, but twice? The occasional absurdity of early large language models was easy to spot and easy to dismiss. Hallucinations were alarming yet seemed reasonably straightforward to detect. But the landscape is quickly changing.

Today’s hallucinations are much more subtle. Just like AI-generated images and videos have become harder to detect, language models now produce errors that blend in with real information.

Take this sentence: “In 2024, OpenAI’s o5 model was the second most popular AI chatbot.” Sounds believable. You’ve heard of OpenAI. Maybe you missed a model release. The name “o5” feels right, but it’s complete fiction. There is no o5 mode, this is just a fake fact wrapped in familiar context.

This strain of hallucination is among the most difficult to catch. The scaffolding is real: known names, expected tone, technical-sounding language. But a single fabricated detail slips through. It’s small enough to be overlooked, but big enough to be repeated.

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Image generated with Google Veo

These hallucinations aren’t just theoretical, they’re being published regularly.

There are countless examples: a fake quote in a newsletter, an invented subcommittee in a policy brief, a footnote to a study that doesn’t exist, etc. Sometimes they’re caught and corrected after a social media callout or fact-check. But for every one that is caught, there are doubtless many others that aren’t.

Once published, they become part of the data landscape, tripping up not just unsuspecting humans, but other LLMs too. The next LLM that scrapes the web for training or indexing may encounter that hallucinated detail and treat it as fact. That second model includes it in its output. Now it exists in two places, and sounds even more credible.

This is how a feedback loop forms:

  1. An LLM generates a plausible but false claim.
  2. That claim is published, becoming a new data point.
  3. Another model consumes the data and uses it to generate new content.
  4. The cycle repeats, normalizing the original error.

In domains with limited primary sources, the risk compounds. A single unchecked hallucination can cascade through reports, summaries, or knowledge bases, shaping perceptions and decisions downstream. Without clear mechanisms to label, isolate, or trace generative content, we risk creating a reality with increasingly artificial edges.

Solving hallucinations isn’t just a model problem, it’s a product one.

The issue isn’t that a model occasionally invents something. It’s that we sometimes let that invention sneak into spaces where accuracy matters. A language model can help summarize a dataset, but it shouldn’t generate the data itself. It can write a headline, but it shouldn’t fabricate the numbers underneath.

This partly comes down to system design. When output needs to be grounded in fact—like demographic insights, policy summaries, or financial overviews—there are a few strategies worth considering to keep things in the realm of reason.

One approach is to set clear boundaries on what the model is allowed to produce. If a dataset includes five demographic groups, then the output should only reflect those five. The model shouldn’t be able to invent a sixth group or remix the structure of the data. This kind of restraint keeps the system aligned with known inputs and makes the output easier to validate.

Another is transparency. Not just in how the model works behind the scenes, but in how its outputs are presented. Users should be able to see what’s grounded in real data, what was inferred, and what was generated. That doesn’t mean exposing every internal mechanism—it means presenting information in a way that invites inspection. When users understand the shape of the system, they’re more likely to trust the output—and better equipped to challenge it when something looks off.

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Image generated with Google Veo

AlphaVu is Using Smarter Design to Prevent Hallucinations

These challenges were front of mind as we designed and built our agentic chat system called “Ask Your Data”(AYD).

One of the foundational decisions was to root AYD in a structured engagement dataset that serves as a clear and accessible source of truth—dramatically reducing the model’s tendency to hallucinate. Importantly, the same dataset powering AYD is also available to clients through dashboards in DataVu, allowing users to verify specific claims immediately rather than having to take AYD’s word for it.

We also embedded guardrails into the system itself: each field in the dataset has a natural-language description written by humans, helping the model interpret the data accurately. The UI includes a “Steps” log that shows how AYD reached its conclusion whether it filtered by keyword, analyzed sentiment, or aggregated across timeframes.

And critically, AlphaVu’s Customer Insights team plays an active role in this process. Analysts regularly review AYD’s responses, provide feedback, and flag issues, ensuring expert human oversight remains part of the loop. That feedback cycle is constant and intentional, and it’s part of how we continue to improve the product.

Learning as We Go

Naturally, as agentic systems mature across the industry, patterns are beginning to emerge that offer useful guideposts for builders.

Frameworks like 12-Factor Agents and other evolving standards are helping teams create more robust, reliable systems. While many of these ideas were already baked into AYD or added through our own experience, we’re continuing to refine our approach in light of this growing body of collective knowledge.

We’re committed to contributing to, and learning from, this ongoing dialogue, as the field evolves through the inevitable cycle of experimentation, mistakes, and progress.

Hallucinations don’t seem to be going away any time soon.

In fact, as models are used more widely, they appear to be getting more common and sometimes harder to detect. For now, we still need real humans in the loop.

Sometimes that’s a casual user noticing something strange. Sometimes it’s an expert doing a deeper review. Either way, designing systems that reduce the window of opportunity for hallucinations—and help users clearly understand, question, and verify what the model produces—is a practical step toward making these tools more reliable.

If there is a silver lining, it’s that the very existence of hallucinations that’s forcing us to build AI products that are more accountable, useful, and reflective of the collaborative role AI is meant to play alongside people.

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