How to Actually Solve AI Hallucinations
Why hallucinations appear, why they're predictable in real environments, and what practical techniques teams use to reduce or control them.
You've just asked your AI coding assistant to help refactor a data pipeline. The code it hands back looks clean, reads well, runs well and even includes helpful comments. There's just one problem: the data it's transformed as part of your pipeline doesn't exist. Welcome to the world of AI hallucinations, where confidence and correctness are two very different things.
If you're a data scientist or ML engineer using AI assistants in your daily workflow, hallucinations aren't a theoretical risk. They're a practical, measurable reality that your team is almost certainly encountering right now.
The Numbers Don't Lie
The scale of the problem is becoming increasingly well-documented. CodeRabbit's State of AI vs Human Code Generation report (December 2025), which analysed 470 open-source GitHub pull requests, found that AI-authored code produces 1.7x more issues than human-written code. That includes 1.75x more logic and correctness errors, 1.57x more security findings, and 1.64x more code quality and maintainability issues.
Meanwhile, the Stack Overflow 2025 Developer Survey reveals a striking paradox: 84% of developers now use or plan to use AI tools, but only 29% trust those tools to produce accurate output, down from 40% the year before. The most commonly cited frustration, reported by 66% of developers? Code that is "almost right, but not quite."
For data science teams specifically, the risks compound. A USENIX Security 2025 study tested 16 code-generating LLMs and found that nearly 20% of over 2.2 million packages referenced across 576,000 code samples were entirely hallucinated. Worse still, 43% of those hallucinated package names were repeated consistently across multiple queries, making them predictable and therefore exploitable through what researchers have termed "slopsquatting" attacks.
Why Hallucinations Are Predictable
Here's the thing: hallucinations aren't random. They follow patterns. AI models generate code statistically, not semantically. They don't understand your business logic, your data schema, or why you chose that particular feature engineering approach. They pattern-match against training data, and when the context is ambiguous or domain-specific, they fill in the gaps with confident-sounding guesses.
This is particularly acute in data science and ML work, where pipelines involve complex interactions between code, data, and model behaviour. A general-purpose coding assistant doesn't know that your preprocessing step introduces target leakage, or that the library it's suggesting was deprecated two versions ago. It generates what looks plausible, and it's on you to verify whether it's actually correct.
Verification Is the Real Solution
The most effective mitigation isn't about finding a model that hallucinates less, though improvements are real and ongoing. It's about building verification into your workflow so that hallucinations are caught before they cause damage.
Research increasingly supports this approach. A comprehensive review of hallucination mitigation techniques published in Mathematics (2025) found that the most effective current pattern is to stop treating AI as an oracle and start treating it as a generator inside a verification loop: reduce the model's freedom to improvise, and increase your system's ability to check what was produced.
For data science and ML teams, this means systematic testing at the point of development, not as an afterthought. It means knowing what to test, where to test it, and having the ability to trace how data and code interact throughout your pipeline so that when something goes wrong, you can identify exactly where and why.
This is the approach we've built into Etiq's Data Science Copilot, verification of outputs by default. Does the code written act in the way you've intended? Through building our Lineage followed by testing and validating all data points, verification becomes a natural part of development rather than an additional burden.
Building Confidence, Not Just Code
The gap between AI adoption and AI trust is real, and it's growing. Closing that gap doesn't require abandoning AI tools. It requires building the verification infrastructure that makes them reliable. For data science teams working with complex pipelines, sensitive data, and high-stakes decisions, that infrastructure isn't optional. It's the difference between shipping models you hope work and shipping models you know work.
Your AI assistant can help you write code faster. But only proper verification ensures that speed doesn't come at the cost of reliability.
Ready to build verification into your data science workflow? Start a free trial of Etiq's Data Science Copilot and see how testing recommendations, lineage, and root cause analysis work together to catch hallucinations before they catch you.
