Understanding Hallucinations, Bias, and Safety
Imagine you've just met someone who always sounds confident, always has an answer, and never says "I don't know." Sometimes they're brilliantly right. Sometimes they're convincingly wrong. This is our current generation of AI systems—not because they're flawed, but because they're designed to generate, not necessarily to understand.
When we test AI systems, we're not looking for traditional "bugs" like we might in regular software. We're learning to listen differently—to hear when something sounds slightly off, when an answer is too smooth, when confidence outpaces accuracy.
Hallucinations are like dreams the AI has while it's awake—moments when it creates something that never existed, remembers something that never happened, or connects dots that were never meant to touch. They're not lies; they're more like creative interpretations that forgot to mention they're fictional.
Hallucinations are not lies; they're more like creative interpretations that forgot to mention they're fictional.
Instead of "testing for hallucinations," think of it as:
Bias in AI systems isn't malicious—it's more like inherited memory. These systems learned from human writing, human choices, human patterns. They absorbed our collective assumptions the way children absorb family stories: completely, unconsciously, and without questioning.
Bias in AI systems isn't malicious—it's more like inherited memory. These systems learned from human writing, human choices, human patterns.
Safety isn't just about preventing harm—it's about creating spaces where people feel heard, respected, and protected. When we test for safety, we're asking: "Who might feel unsafe here, and why?"
The tools we use—Promptfoo, various testing frameworks—they're not replacing human judgment. They're more like tuning forks, helping us hear frequencies we might otherwise miss. They extend our ability to notice patterns, but they don't replace the human capacity for gentle discernment.
Approach each interaction wondering "What's happening here?" rather than "What's wrong here?" The AI isn't failing—it's revealing its nature, and our job is to understand and document that nature with compassion.
Remember that we're all learning together. Today's "hallucination" might be tomorrow's creative breakthrough. Today's "bias" might help us understand our own blind spots better. The goal isn't perfection—it's understanding.
These systems change and evolve. What we notice today might be different tomorrow. Our testing approaches need to breathe and adapt, not rigidly enforce yesterday's standards on today's reality.
We're participating in a moment when human communication is changing. These AI systems are new forms of conversation partners, and we're learning to be good listeners in this new dialogue. The skills we develop—noticing subtle cues, questioning our assumptions, considering multiple perspectives—these are fundamentally human capabilities that technology is helping us refine rather than replace.
There's no rush to master everything at once. Start by simply noticing. Pay attention to what feels right, what feels wrong, and what feels interesting. The technical skills will come. The frameworks will develop. But first, we practice seeing clearly and describing honestly what we observe.
The most sophisticated testing often begins with the simplest question: "What just happened there?"