My Arduino-powered toy car was the perfect combination of genius and self-destruction. Despite being equipped with sensors to detect obstacles, it would calculate its path and then… crash directly into the nearest wall. Every. Single. Time.
Table of Contents
- Sir Smashalot: My Self-Driving Disaster
- When AI Becomes That Overconfident Guy at the Party
- The Real Bug: Blind Data, Confident Decisions
Funny thing about AI hallucinations: they often start the same way my toy car did: with confident decisions built on garbage data.
Some of you may recall my conference talk, “I put a carnivorous plant on the Internet of Things to save its life, and it did not survive”. While working on that project, I also attempted to build an Arduino-controlled toy car. Its erratic behavior quickly earned it a name: Sir Smashalot.
Sir Smashalot: My Self-Driving Disaster
I could control the speed of the front wheels separately to make it turn in a way similar to how tanks turn, and I equipped it with ultrasonic sensors (a poor man’s LIDAR). The car could use the sensors to calculate the distance to the obstacles and turn to avoid hitting them. Could. But Sir Smashalot decided to forgo that ability and charged into walls like a toddler on espresso.
You could practically hear the car thinking, “Obstacle detected. Accelerate.” Like it mistook danger for a green light. So, a coding error? Nope. The code was correct, but something was off. I watched, first puzzled, then laughing, then increasingly frustrated as my creation repeatedly committed mechanical suicide.
As usual, the problem was data quality. The sensors detected the obstacles, but it was too late to matter. This wasn’t just a coding problem. Just like the humidity sensors that doomed my IoT carnivorous plant, the car was executing perfect logic on garbage input (lack of brakes probably didn’t help either…).
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When AI Becomes That Overconfident Guy at the Party
Today, years later, I sit watching Gemini gather data from a few dozen documents. For 20 minutes, it analyzes information, references sources, and builds what appears to be a comprehensive answer. Its answer turns out to be a completely hallucinated analysis with fabricated statistics. This cutting-edge AI was just my wall-crashing Arduino car in a much more expensive package.
Companies invest millions in sophisticated RAG systems that ingest vast document libraries and create impressive knowledge graphs. Their demos look flawless. Yet in production, their AI confidently processes all that data and then delivers completely hallucinated nonsense to customers with the same unwavering confidence my car had while driving into walls (or chairs and my feet…).
The irony wasn’t lost on me: my $30 toy car was at least honest about its failures. It crashed visibly and immediately. The multi-billion-dollar AI conceals its failures behind a veneer of articulate confidence. At least my car wore its failure like a badge of honor (and a few dents). The AI, though? It’s the overconfident guy at the party quoting made-up stats with a glass of wine in hand.
These aren’t edge cases but symptoms of a systemic flaw. Fixing AI hallucinations starts with challenging the assumptions behind the data inputs.
The Real Bug: Blind Data, Confident Decisions
Systems don’t crash (both literally and figuratively) because they lack data. They fail because they can’t discern which data matters when it matters.
My car didn’t need more sensors. It needed better sensors (and brakes…). Similarly, the builders of today’s AI don’t need larger context windows or more document repositories. Instead, they should focus on systematic error analysis to understand exactly why and when AI hallucinates. That’s the heart of any real solution to AI hallucinations: uncovering the mismatch between data inputs and model behavior.
Most engineers continue to add more data sources, tweak prompts, or implement complex guardrails. It’s equivalent to giving my car more sensors while ignoring that those sensors were only slightly better than using random number generators. Tinkering with prompts while ignoring data quality is like slapping a racing stripe on Sir Smashalot and expecting it to steer better. Cosmetics over causes. Breakthroughs come when we obsessively analyze the failures instead of blindly adding complexity.
Whether you’re a weekend tinkerer building crash-prone Arduino cars or an engineer wrestling billion-dollar AI models, the fix starts the same way: not with more code, but with better questions.
Systematic error analysis isn’t just a process — it’s a mindset. One that replaces blind faith in more data with relentless curiosity about why systems fail.
If AI is the brain, data is the senses. And right now, we’re building supercomputers with blindfolds on.
I’ve documented the exact approach in my article “AI Evaluation Best Practices: Why Data Analysis Matters For Systematic AI Improvements”. If you’re building or maintaining AI systems and tired of hallucinated answers, my systematic error analysis framework is built to help you make your AI trustworthy.
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