How we reduced hallucinations in Open Models from 67% to 11%

Shafwan safi — Wed, 03 Jun 2026 10:27:32 +0000

After spending months building AI applications, one thing became painfully obvious:

The hardest part isn't getting an LLM to work.

It's getting it to work reliably.

We kept running into the same issues:

So we started building a reliability layer for AI applications.

Over the last few months we've built Crukx, which combines:

• Hallucination detection and correction
• Self-healing workflows
• Autonomous codebase auditing
• Prompt optimization
• Runtime guardrails

One result we're particularly happy with:

Our hallucination benchmark went from roughly 67% hallucination rate to around 11% using a layered verification and correction pipeline.

We're still early and there are plenty of things that don't work perfectly yet.

I'd genuinely love feedback from people building with LLMs:

What's been your biggest reliability challenge in production?

Happy to answer technical questions about the architecture and benchmarks.

DEV Community: Shafwan safi