A few months ago, I noticed something frustrating while building AI applications:
The model response looked perfect.
The JSON was valid.
The schema matched.
The API returned 200.
But the output was still wrong.
A field was missing.
A value didn't make sense.
A business rule was violated.
The dangerous failures are not the obvious ones.
It's the responses that look correct enough to pass through your system.
That's the gap I started exploring:
How do we decide when an AI output is actually safe to use?
Most teams today add:
custom validation code
retry loops
manual checks
another LLM to review the output
But reliability shouldn't be an afterthought added to every AI workflow.
I'm building Linden, an AI reliability layer designed to sit between LLM outputs and production systems.
The idea:
Every AI response gets evaluated before your application trusts it.
ALLOW → continue
WARN → continue with caution
REGENERATE → attempt recovery
BLOCK → prevent the output
The goal isn't to make AI perfect.
The goal is to make AI systems safer to build with.
I'd love to hear from engineers:
What's the most painful LLM failure you've dealt with in production?