AI Reliability
Why AI Performs Better When Results Are Testable
AI becomes more dependable when the result can collide with reality: run the code, measure the object, open the page or check the source.
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AI Reliability
AI can be useful and still be wrong in convincing ways. These experiments focus on verification, self-review, repeated mistakes, unintended changes, and the conditions that make trust more reasonable.
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Can AI Check Its Own Work? Where Self-Review Fails
AI self-review can catch mistakes, but it isn't independent verification. Here's where checking its own work helped us—and where it failed.
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AI Reliability
When Should You Trust AI Without Double-Checking?
Some AI output can be used with light review. The key is reversibility, consequence, factual dependence and whether you can notice a bad result.
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AI Reliability
What Is AI Bad At? The Problems We Keep Running Into
Our AI failures kept repeating the same patterns: confident errors, drift, hidden dependencies, inconsistency and unnecessary complexity.
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AI Reliability
How to Verify AI Answers Without Checking Everything Manually
You do not need to verify every AI sentence equally. Use consequence, uncertainty and external tests to focus checking where it matters.
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AI Reliability
Can AI Recognize When Its Own Answer Is Bad?
AI can often identify weaknesses in its own response when asked to critique it, but self-critique is not the same as independent verification.
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AI Reliability
Why AI Can Sound Right Even When the Result Is Wrong
AI can produce fluent, confident explanations that are still wrong. We learned to separate plausibility from evidence and test important outputs.
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AI Reliability
Why AI Sometimes Changes Things You Didn’t Ask It to Change
AI can solve the requested problem while also changing nearby content, code, or visuals. Learn why it happens and how preservation constraints reduce the risk.
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AI Reliability
Why AI Keeps Repeating Mistakes You Already Corrected
Correcting AI once doesn't guarantee the mistake is gone. We learned to separate one-time corrections from project rules and workflow failures.
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